Connect

8 clients · secrets stay placeholders

JSON (mcpServers)Claude CodeCodexOpenCodeCursorVS CodeGemini CLIGoose

{
  "mcpServers": {
    "terradev": {
      "type": "sse",
      "url": "https://terradev-mcp.terradev.cloud/sse"
    }
  }
}

claude mcp add --transport sse terradev https://terradev-mcp.terradev.cloud/sse

[mcp_servers.terradev]
url = "https://terradev-mcp.terradev.cloud/sse"

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "terradev": {
      "type": "remote",
      "url": "https://terradev-mcp.terradev.cloud/sse",
      "enabled": true
    }
  }
}

{
  "mcpServers": {
    "terradev": {
      "type": "sse",
      "url": "https://terradev-mcp.terradev.cloud/sse"
    }
  },
  "deeplink": "cursor://anysphere.cursor-deeplink/mcp/install?name=terradev&config=eyJ0eXBlIjoic3NlIiwidXJsIjoiaHR0cHM6Ly90ZXJyYWRldi1tY3AudGVycmFkZXYuY2xvdWQvc3NlIn0%3D"
}

{
  "servers": {
    "terradev": {
      "type": "sse",
      "url": "https://terradev-mcp.terradev.cloud/sse"
    }
  }
}

{
  "mcpServers": {
    "terradev": {
      "url": "https://terradev-mcp.terradev.cloud/sse"
    }
  }
}

extensions:
  "terradev":
    enabled: true
    name: "terradev"
    type: sse
    uri: "https://terradev-mcp.terradev.cloud/sse"
    timeout: 300

Quality 57 / 100needs work

protocol50 · weight 25

warn
protocol.modernnewest supported version is 2025-11-25; 2026-07-28 not supported, older versions are deprecated until 2027-07-28
n/a
protocol.statelessonly applies to 2026-07-28 servers
warn
protocol.transportonly the legacy HTTP+SSE transport, deprecated until 2027-07-28
n/a
protocol.list_ttlonly applies to 2026-07-28 servers

authorizationn/a · weight 15

n/a
auth.prm, auth.as_metadata, auth.cimdno remote uses OAuth
n/a
auth.secret_in_urlno templated URL

tool hygiene50 · weight 30

warn
tools.descriptions3 of 277 tools have a description under 20 characters
pass
tools.description_lengthdescriptions are concise
pass
tools.schemasevery tool has an object input schema
fail
tools.annotationsno tool declares readOnlyHint or destructiveHint, so clients cannot tell safe calls from risky ones
fail
tools.directory_hintsno tool declares all four of readOnlyHint, destructiveHint, idempotentHint and openWorldHint; missing: up (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); governance_record_consent (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); infer_failover (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); …
fail
tools.token_costabout 38,007 tokens to load every tool, a large share of an agent's context
pass
tools.api_dumptools are not a one-to-one API dump

stability75 · weight 15

warn
stability.changes32 tool changes in 30 days
pass
stability.rug_pullno tool changed its meaning under the same name

dependencies67 · weight 15

fail
deps.known_vulns2 advisories to act on (malicious, or rated high or critical with a fix available): GHSA-2g87-pp6c-29x5 in [email protected] (direct, fixed in 3.14.4), GHSA-5wr8-gcqf-gqf3 in [email protected] (direct, fixed in 3.14.4); 1 more of lower severity or without a fix
pass
deps.mcp_sdk_versionmcp 1.30.0, no known advisory
pass
deps.resolvable[email protected] resolved: 66 packages

277 tools, about 38,007 tokens to load them all · tool set 9f07aeadf921 · tools last changed 2026-09-29 · checked 2026-10-20 04:09 UTC

Tool changes

2026-09-29triggers_historyadded
Show trigger execution history.
2026-09-29triggers_executeadded
Manually execute a trigger now.
2026-09-29triggers_enableadded
Enable or disable a trigger.
2026-09-29triggers_listadded
List configured triggers.
2026-09-29triggers_createadded
Create an event trigger (schedule/webhook/metric).
2026-09-29quote_gpuadded
Get a GPU price quote across providers.
2026-09-29preflight_provisionadded
Run preflight validation before provisioning GPU instances.
2026-09-29migrate_compareadded
Compare migration target providers by cost/compatibility.
2026-09-29migrate_rollbackadded
Roll back a migration to the source provider.
2026-09-29migrate_statusadded
Show status of running/completed migrations.

Showing the latest 10. Full history: list_changes · trend: quality JSON.

Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed. How it is computed.

Trust 90 / 100

provenance75

liveness100

freshness85

hygiene100

+repository +namespace-matches-repo -thin-description +reachable +uptime-100% ~updated-40d-ago

Derived from observable signals (official registry feed and our own credential-free probes); no code audit performed. How it is computed.

Dependencies

[email protected] PyPI65 dependencies, resolved · MCP SDK mcp 1.30.0 · advisories checked 2026-10-19

advisory	severity	package	fixed in
GHSA-2g87-pp6c-29x5aiohttp: Multiple instances of excessive or unbounded memory/CPU use	high	`[email protected]`direct	fixed in `3.14.4`
GHSA-5wr8-gcqf-gqf3aiohttp server lacks an initial request/header timeout	high	`[email protected]`direct	fixed in `3.14.4`
GHSA-494j-54xm-rwp3AIOHTTP: Secure attribute not enforced for shared cookie	low	`[email protected]`direct	fixed in `3.14.4`

Dependency graphs from deps.dev; advisories from OSV.dev, including the GitHub Advisory Database and the PyPI Advisory Database; all CC BY 4.0. The graph is what a clean install of this version resolves today; nothing was installed or run. How it is checked.

Endpoints

remote	auth	reachable	uptime 30d	p50	protocol	last ok
`https://terradev-mcp.terradev.cloud/sse` sse	none	yes	100%	263 ms	2025-11-25	2026-10-19

package	registry	version	runtime	secrets
`terradev-cli`	pypi	6.2.20	uvx	`RUNPOD_API_KEY`, `VAST_API_KEY`, `LAMBDA_API_KEY`, `HF_TOKEN`, `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, `TERRADEV_MCP_BEARER_TOKEN`

Tools (277)

`active_context`	Get current Terradev state: running training jobs, active instances, spend-to-date, alerts. Call this on session start to resume context from previous sessions.
`agent_agentic_serving_configure`	Configure agentic inference serving settings.
`agent_agentic_serving_helm_values`	Print Helm values for agentic inference deployment.
`agent_agentic_serving_k8s`	Print K8s deployment manifests for agentic inference.
`agent_agentic_serving_launch_args`	Print engine launch arguments for copy-paste.
`agent_agentic_serving_lmcache_env`	Print LMCache environment variables.
`agent_agentic_serving_show_config`	Show current agentic serving configuration.
`agent_cost`	Show real-time cost breakdown for a fleet by tier.
`agent_deploy`	Provision a heterogeneous agent fleet across all tiers simultaneously.
`agent_langchain_create_langgraph`	Create a LangGraph workflow.
`agent_langchain_create_pipeline`	Create an SGLang pipeline.
`agent_langchain_create_workflow`	Create a LangChain workflow.
`agent_langchain_test`	Test connection to LangChain service.
`agent_langgraph_create_workflow`	Create a LangGraph workflow.
`agent_langgraph_deploy`	Deploy a workflow.
`agent_langgraph_status`	Get workflow status.
`agent_langgraph_test`	Test connection to LangGraph service.
`agent_letta_chat`	Send a message to a Letta agent.
`agent_letta_create`	Create a new stateful Letta agent.
`agent_letta_delete`	Delete a Letta agent.
`agent_letta_list`	List Letta agents.
`agent_letta_remember`	Teach a Letta agent a durable fact.
`agent_letta_status`	Show the state of a Letta agent.
`agent_list`	List all known agent fleets.
`agent_mem0_add`	Store a memory in Mem0 for an agent or user.
`agent_mem0_configure`	Configure Mem0 credentials and defaults.
`agent_mem0_delete`	Delete a memory by ID.
`agent_mem0_forget`	Delete all memories matching an entity scope.
`agent_mem0_get`	Get a single memory by ID.
`agent_mem0_list`	List memories for an entity scope.
`agent_mem0_search`	Search agent/user memories.
`agent_mem0_test`	Test connection to Mem0.
`agent_mem0_update`	Update a memory by ID.
`agent_plan`	Plan a heterogeneous agent fleet without provisioning.
`agent_scale`	Scale a single fleet tier up or down without affecting other tiers.
`agent_skill_attach`	Attach a skill.md to a Letta agent as a durable memory block.
`agent_skill_init`	Create a skill.md template for an agent.
`agent_status`	Show live status of a fleet — tier health, KV hit rate, queue depth, cost.
`agent_teardown`	Terminate all fleet instances and remove fleet state.
`agent_vector_db_down`	Teardown a vector database provisioned for an agent fleet.
`agent_vector_db_up`	Provision a vector database for an agent fleet.
`analytics`	Get cost analytics
`checkpoint_delete`	Delete a checkpoint.
`checkpoint_list`	List all checkpoints for a training job.
`checkpoint_promote`	Promote a checkpoint to a final model path for serving.
`checkpoint_restore`	Restore a specific checkpoint for a training job.
`checkpoint_save`	Manually trigger a checkpoint save for a running training job.
`configure_provider`	Configure provider credentials
`cost_analyze`	Deep cost analysis of current GPU infrastructure: per-provider breakdown, utilization efficiency, waste identification, and optimization potential.
`cost_optimize_recommend`	Generate actionable cost optimization recommendations: spot migration, GPU right-sizing, provider arbitrage, idle shutdown, and density packing.
`cost_simulate`	Simulate cost optimization scenarios with ROI projections. Compare current vs optimized infrastructure costs.
`create_postgresql_connection`	Create a PostgreSQL database connection with auto-table creation. Returns a connection ID for subsequent operations.
`create_sqlite_connection`	Create a SQLite database connection with auto-table creation. Returns a connection ID for subsequent operations.
`database_weaviate_create_collection`	Create a Weaviate collection.
`database_weaviate_delete_collection`	Delete a Weaviate collection.
`database_weaviate_hybrid_search`	Hybrid vector + BM25 search in a Weaviate collection.
`database_weaviate_insert`	Insert objects into a Weaviate collection.
`database_weaviate_list_collections`	List Weaviate collections.
`database_weaviate_query`	Vector similarity search in a Weaviate collection.
`database_weaviate_up`	Initialize a Weaviate connection.
`deepeval_evaluate`	Evaluate a single LLM output with a DeepEval metric (AnswerRelevancy, Faithfulness, Hallucination, etc.).
`deepeval_init`	Generate a starter DeepEval test file.
`deepeval_metrics`	List available DeepEval metrics for LLM evaluation.
`deepeval_run`	Run a DeepEval test suite from a Python test file.
`dvc_diff`	Show DVC diff between two revisions (e.g. training checkpoints). Shows added, modified, deleted files.
`dvc_push`	Push DVC-tracked data to the configured remote storage.
`dvc_stage_checkpoint`	Atomic checkpoint staging: DVC add + push + git commit in one operation. Promotes a training checkpoint to versioned storage.
`dvc_status`	Get DVC repository status: tracked files, remotes, and changes since last commit.
`egress_cheapest_route`	Find the cheapest egress route between cloud providers/regions for model weights or dataset transfer. Supports multi-hop routing.
`egress_optimize_staging`	Optimize dataset or model staging across regions by finding the cheapest transfer plan. Integrates with the dataset stager for parallel uploads.
`environments_approve`	Approve a pending environment promotion.
`environments_create`	Create a named deployment environment (dev/staging/prod) with config.
`environments_history`	Show promotion/change history for an environment.
`environments_list`	List deployment environments.
`environments_promote`	Promote a workload or config between environments.
`get_database_connection`	Get information about a database connection including type, status, and configuration.
`governance_compliance_report`	Generate comprehensive compliance report: consent stats, policy evaluations, data movements, violations. For GDPR/SOC2/HIPAA audits.
`governance_evaluate_opa`	Evaluate OPA (Open Policy Agent) policies for data access. Checks region restrictions, classification rules, and compliance requirements.
`governance_move_data`	Move data with full governance audit trail. Requires prior consent and OPA policy approval. Tracks integrity, encryption, and compliance.
`governance_movement_history`	Get data movement audit log. Filter by user, dataset, or time range.
`governance_record_consent`	Record a consent response (granted or denied) for a pending consent request.
`governance_request_consent`	Request user consent for data movement across cloud regions. GDPR/SOC2 compliant consent tracking with audit trail.
`gpu_topology`	GPU NUMA topology report with intra-GPU XCD (Accelerated Compute Die) awareness. Models MI300X (8 XCDs, 192GB HBM3), MI300A (6 XCDs, 128GB), H200 (unified 141GB HBM3e), H100 (80GB). Reports PCIe locality (PIX/PXB/PHB/SYS), GPU-NIC pairing, SR-IOV VF status, and generates XCD-aware NCCL/AITER environment variables.
`guardrails_chat`	Send a message through NeMo Guardrails and return the safety-filtered response. Applies topical, jailbreak, PII, and factcheck rails.
`guardrails_generate_config`	Generate default Colang 2.x guardrails configuration files (topical, jailbreak, PII, factcheck rails).
`guardrails_k8s`	Generate Kubernetes deployment manifest for NeMo Guardrails server (standalone or sidecar mode).
`guardrails_test`	Test connection to NeMo Guardrails server.
`helm_generate`	Generate Helm charts from workload specifications.
`hf_create_endpoint`	Create a HuggingFace Inference Endpoint (paid GPU endpoint). Supports custom GPU types, regions, and scaling.
`hf_delete_endpoint`	Delete a HuggingFace Inference Endpoint.
`hf_endpoint_infer`	Run inference on a HuggingFace Inference Endpoint. Supports text generation, embeddings, and custom inputs.
`hf_endpoint_info`	Get detailed info about a specific HuggingFace Inference Endpoint: status, URL, scaling config, cost.
`hf_hardware_compare`	Compare all hardware options for a HuggingFace model. Returns side-by-side cost, performance, and compatibility analysis.
`hf_hardware_recommend`	Get hardware recommendation with cost breakdown for any HuggingFace model. Returns optimal GPU type, estimated cost, and performance score.
`hf_list_datasets`	Search and browse HuggingFace Hub datasets. Filter by author and search query.
`hf_list_endpoints`	List all active HuggingFace Inference Endpoints with status, URL, and cost.
`hf_list_models`	Search and browse HuggingFace Hub models. Filter by author, task, library. Returns model ID, downloads, likes, and tags.
`hf_model_info`	Get detailed model info: architecture, size, downloads, license, tags, pipeline_tag, and model card.
`hf_smart_template`	Auto-generate an optimized deployment template for any HuggingFace model. Analyzes model size, architecture, and quantization to select optimal hardware and generate ready-to-deploy configs.
`hf_space_deploy`	Deploy model to HuggingFace Spaces
`hf_space_status`	Get HuggingFace Space deployment status.
`infer_failover`	Run health checks and auto-failover for inference endpoints. If a primary endpoint is unhealthy and has a backup configured, traffic automatically shifts to the backup provider.
`infer_route`	Semantic-aware inference routing. Analyzes query content across 6 signal dimensions (modality, complexity, domain, language, safety, keywords), applies NUMA-aware endpoint scoring, and selects the optimal inference endpoint. Uses DAG parallel execution for signal extraction.
`infer_route_disagg`	Disaggregated Prefill/Decode routing (DistServe architecture). Splits LLM inference into compute-bound prefill phase (routed to FLOPS-optimized GPUs like H100 SXM) and memory-bound decode phase (routed to bandwidth-optimized GPUs like MI300X). Tracks KV cache handoffs between endpoint pairs.
`inferx_configure`	Configure InferX serverless platform credentials.
`inferx_delete`	Delete an InferX model deployment.
`inferx_deploy`	Deploy model to InferX serverless platform
`inferx_list`	List deployed InferX models
`inferx_optimize`	Get cost analysis for inference endpoints
`inferx_quote`	Get InferX pricing quotes for a GPU type.
`inferx_status`	Check InferX endpoint status
`inferx_usage`	Get InferX account usage statistics: requests, cost, GPU hours, latency.
`k8s_create`	Create Kubernetes cluster with GPU nodes for optimal multi-cloud deployment
`k8s_destroy`	Destroy a Kubernetes cluster
`k8s_device_plugin`	Configure Kubernetes GPU device plugin settings: time-slicing, MIG strategy, and resource naming.
`k8s_gpu_operator_install`	Install NVIDIA GPU Operator on a Kubernetes cluster. Configures driver containers, device plugin, DCGM exporter, and GPU Feature Discovery.
`k8s_info`	Get information about a specific cluster
`k8s_list`	List Kubernetes clusters
`k8s_mig_configure`	Configure Multi-Instance GPU (MIG) partitioning on A100/H100 GPUs. Splits a single GPU into isolated instances for multi-tenant workloads.
`k8s_time_slicing`	Configure GPU time-slicing for Kubernetes. Allows multiple pods to share a single GPU with configurable oversubscription.
`karpenter_create_nodepool`	Create a Karpenter GPU NodePool.
`karpenter_delete_nodepool`	Delete a Karpenter NodePool.
`karpenter_events`	Show recent Karpenter scaling events.
`karpenter_gpu_nodes`	List GPU nodes provisioned by Karpenter.
`karpenter_install`	Install Karpenter on an EKS cluster for GPU node autoscaling.
`karpenter_logs`	Show Karpenter controller logs.
`karpenter_nodepools`	List Karpenter NodePools.
`karpenter_resources`	Show Karpenter resource usage and capacity.
`karpenter_status`	Show Karpenter installation/controller status.
`kserve_generate_yaml`	Generate a GPU-aware KServe InferenceService YAML manifest with NUMA pinning, resource limits derived from model size and VRAM, and topology hints.
`kserve_list`	List KServe InferenceServices in a Kubernetes namespace.
`kserve_status`	Get detailed status of a KServe InferenceService including readiness, traffic split, and URL.
`kv_cache_efficiency`	Analyze KV cache efficiency across serving endpoints.
`langchain_create_sglang_pipeline`	Create an SGLang model-serving pipeline via LangChain. Connects LangChain agents to SGLang inference endpoints.
`langchain_create_workflow`	Create a LangChain workflow.
`langfuse_configure`	Configure Langfuse credentials (public key, secret key, host URL).
`langfuse_datasets`	List Langfuse datasets for evaluation and fine-tuning.
`langfuse_export_training_data`	Export Langfuse traces as instruction/response pairs for LoRA fine-tuning. Filters by quality score.
`langfuse_k8s`	Generate Kubernetes deployment manifest for self-hosted Langfuse.
`langfuse_otel_env`	Print OTEL environment variables for instrumenting LLM apps to send traces to Langfuse.
`langfuse_quality`	Get aggregated quality metrics from Langfuse scores for drift detection.
`langfuse_score`	Create an evaluation score for a Langfuse trace (e.g. quality, accuracy, relevance).
`langfuse_scores`	List evaluation scores from Langfuse, optionally filtered by trace or score name.
`langfuse_test`	Test Langfuse connectivity and list accessible projects.
`langfuse_trace`	Get a single Langfuse trace with all observations/spans.
`langfuse_traces`	List recent LLM traces from Langfuse.
`langgraph_create_workflow`	Create a LangGraph stateful workflow with monitoring. Supports agent graphs, tool calling, and state persistence.
`langgraph_evaluation_workflow`	Create an evaluator-optimizer workflow in LangGraph. Generates outputs, evaluates quality, and iteratively improves.
`langgraph_orchestrator_worker`	Create an orchestrator-worker pattern workflow in LangGraph. The orchestrator delegates tasks to specialized worker agents.
`langgraph_workflow_status`	Get the status and metrics of a LangGraph workflow execution.
`lineage_checkpoint`	Record a data/model lineage checkpoint.
`lineage_diff`	Diff lineage between two artifacts or checkpoints.
`lineage_export`	Export lineage data (JSON/graph) for an artifact.
`lineage_graph`	Render the lineage graph for an artifact.
`lineage_trace`	Trace the lineage of a dataset or model artifact.
`local_scan`	Scan local machine and network for available GPU devices. Returns total VRAM pool for local-first provisioning.
`lora_add`	Hot-load a LoRA adapter onto a running vLLM endpoint. The adapter becomes immediately available as a model name for inference requests. Uses vLLM's fused_moe_lora kernel for 454% higher output tokens/sec on MoE models.
`lora_list`	List LoRA adapters loaded on a running vLLM endpoint. Shows base models and hot-loaded fine-tuned adapters.
`lora_remove`	Hot-unload a LoRA adapter from a running vLLM endpoint. Frees GPU memory for other adapters.
`manage_instance`	Manage GPU instances (stop/start/terminate)
`manifests`	List cached manifests and versions for jobs.
`migrate_compare`	Compare migration target providers by cost/compatibility.
`migrate_execute`	Execute a previously-planned workload migration.
`migrate_plan`	Plan a workload migration between providers with cost analysis.
`migrate_rollback`	Roll back a migration to the source provider.
`migrate_status`	Show status of running/completed migrations.
`mlflow_list_experiments`	List MLflow experiments on the configured tracking server.
`mlflow_log_run`	Log a Terradev training run to MLflow with auto-injected GPU type, provider, cost/hr, and duration as params.
`mlflow_register_model`	Register a trained model in the MLflow model registry with Terradev provenance tags.
`ml_vllm_lora_link`	Load the active registry version of an adapter onto a vLLM server.
`ml_vllm_lora_list`	List LoRA adapters currently loaded on a vLLM server.
`ml_vllm_lora_load`	Hot-load a LoRA adapter onto a running vLLM server.
`ml_vllm_lora_sync`	Synchronize an adapter from the registry across multiple vLLM replicas.
`ml_vllm_lora_unload`	Hot-unload a LoRA adapter from a running vLLM server.
`moe_deploy`	Deploy Mixture-of-Experts models with production-ready cluster templates. Auto-applies vLLM cost optimizations (KV cache offloading for up to 9x throughput, MTP speculative decoding for up to 2.8x speed, sleep mode for 18-200x faster restarts). Supports GLM-5, Qwen 3.5, Mistral Large 3, DeepSeek V4, Llama 5. Configures NVLink topology, tensor parallelism, FP8 quantization, vLLM/SGLang backends, and GPU-aware HPA autoscaling.
`ollama_chat`	Chat with an Ollama model using the chat/completions API.
`ollama_generate`	Generate text using an Ollama model (non-chat completions).
`ollama_list`	List models available on an Ollama server.
`ollama_model_info`	Get detailed information about an Ollama model (parameters, template, license).
`ollama_ps`	List currently running Ollama models.
`ollama_pull`	Pull a model to an Ollama server on a remote instance.
`optimize`	Find cheaper alternatives for running instances
`orchestrator_evict`	Evict a model from GPU memory.
`orchestrator_infer`	Test inference with a model via the orchestrator.
`orchestrator_load`	Load a model into GPU memory.
`orchestrator_register`	Register a model with the orchestrator.
`orchestrator_start`	Start the model orchestrator for multi-model GPU sharing with eviction policies.
`orchestrator_status`	Get orchestrator and model status including GPU memory utilization.
`phoenix_k8s`	Generate Kubernetes deployment manifest for self-hosted Arize Phoenix server.
`phoenix_otel_env`	Generate OpenTelemetry environment variables for instrumenting serving pods with Phoenix tracing.
`phoenix_projects`	List Phoenix projects (trace namespaces).
`phoenix_snippet`	Generate Python instrumentation snippet for adding Phoenix tracing to LLM applications.
`phoenix_spans`	List recent spans for a Phoenix project. Supports SpanQuery DSL filters like "span_kind == 'RETRIEVER'" or "status_code == 'ERROR'".
`phoenix_test`	Test connection to Arize Phoenix server. Returns collector endpoint and project count.
`phoenix_trace`	View full execution tree for a specific trace ID. Shows span hierarchy, latencies, and token counts.
`preflight`	Pre-training validation: GPU availability, NCCL, RDMA, drivers across all nodes.
`preflight_gpu_check`	GPU-specific preflight validation: NVIDIA drivers, CUDA version, GPU count, NCCL, NVLink topology, NCU stall-signature profiling, and adversarial config verification (V1-V3).
`preflight_network_check`	Network-specific preflight validation: RDMA availability, InfiniBand status, inter-node bandwidth, latency matrix, firewall rules.
`preflight_provision`	Run preflight validation before provisioning GPU instances.
`preflight_report`	Generate full preflight validation report with pass/warn/fail per check. Covers GPU drivers, CUDA, NCCL, RDMA, network, disk, and Docker.
`price_intel`	GPU price intelligence with quantitative analytics. Computes delta (rate of change), gamma (acceleration), and annualized realized volatility on GPU spot/on-demand prices across 21+ providers. Identifies cheapest time windows and provider arbitrage opportunities.
`price_spot_risk`	Spot instance risk assessment per provider. Returns interruption probability, mean time to interruption, and recommended mitigation.
`price_trends`	Get GPU price trend analysis with delta (rate of change), gamma (acceleration), and annualized volatility. Identifies cheapest time windows.
`provision_gpu`	Provision GPU instances for optimal parallel efficiency
`qdrant_collections`	List all Qdrant vector collections with their point counts and configurations.
`qdrant_count`	Count points (vectors) in a Qdrant collection.
`qdrant_create_collection`	Create a Qdrant vector collection. Auto-configures vector dimensions from embedding model name.
`qdrant_info`	Get detailed info and stats for a Qdrant collection.
`qdrant_k8s`	Generate Kubernetes StatefulSet manifest for self-hosted Qdrant vector database.
`qdrant_test`	Test connection to Qdrant vector database. Returns cluster info and collection count.
`query_database`	Execute a SELECT query on a database connection. Returns query results as a list of dictionaries.
`quote_gpu`	Get a GPU price quote across providers.
`ray_disagg_pd_deploy`	Generate a Ray Serve LLM disaggregated Prefill/Decode deployment. Splits inference into compute-bound prefill and memory-bound decode phases with KV cache transfer via NIXL.
`ray_list_jobs`	List all running Ray jobs and tasks.
`ray_parallelism_strategy`	Compute optimal TP/DP/EP parallelism strategy for a given MoE model and GPU count. Returns recommended configuration with rationale.
`ray_start`	Start a Ray cluster (head node or worker). For distributed ML training and inference.
`ray_status`	Get Ray cluster status including node count, resources, memory, and running jobs.
`ray_stop`	Stop the Ray cluster on the current node.
`ray_submit_job`	Submit a job script to the Ray cluster for distributed execution.
`ray_wide_ep_deploy`	Generate a Ray Serve LLM Wide-EP (Expert Parallel) deployment for MoE models. Returns Python script and config for distributed MoE serving with EPLB and DeepEP.
`rollback`	Explicit versioned rollback. Format: job@version (e.g., llama3@v3).
`run_workflow`	Run a declarative YAML workflow that chains multiple Terradev commands (provision → preflight → train → monitor → checkpoint). Returns step-by-step execution status with cost estimates and confirmation gates for expensive operations.
`secrets_env`	Print environment-style export lines for a provider. By default values are masked.
`secrets_get`	Retrieve a stored secret. By default the value is masked.
`secrets_list`	List stored provider and key names. Values are never shown.
`secrets_remove`	Remove a provider or a single key from the secret store.
`secrets_run`	Run a shell command with secrets injected into the environment.
`secrets_set`	Store a secret in the Terradev secrets store. Values are encrypted at rest.
`secrets_sync`	Import TERRADEV_* environment variables into the secret store for supported cloud providers.
`secrets_verify`	Check which providers are fully configured and which keys are missing.
`setup_provider`	Get setup instructions for a provider
`sglang`	Complete SGLang optimization stack with workload-specific auto-tuning for 7 workload types: agentic chat, batch inference, low latency, MoE models, PD disaggregated, structured output, and RAG.
`sglang_inference`	[DEPRECATED] Use sglang action='inference' instead. Test inference against a running SGLang endpoint.
`sglang_start`	[DEPRECATED] Use sglang action='start' instead. Start an SGLang inference server on a remote instance.
`sglang_stop`	[DEPRECATED] Use sglang action='stop' instead. Stop an SGLang server on a remote instance.
`stage`	Compress, chunk, checksum, and position datasets near compute. Supports local paths, S3/GCS URIs, HTTP URLs, and HuggingFace dataset names. Returns staging plan with agent recommendations.
`status`	View all instances and costs with state optimization
`train`	Launch distributed training on provisioned GPU nodes. Supports torchrun, deepspeed, accelerate, and megatron. Use from_provision='latest' to auto-resolve node IPs from your last provision command.
`train_detect_stragglers`	Detect straggler nodes in distributed training. Identifies GPUs with significantly lower utilization that slow the whole job.
`training_config_generate`	Generate a complete training configuration from a declarative spec. Auto-detects framework, sets optimal parallelism, and configures distributed training.
`training_launch_distributed`	Full distributed training launch with framework auto-detection, topology validation, and monitoring. Combines preflight + train + monitor in one operation.
`train_monitor`	Real-time GPU monitoring for training jobs. Shows utilization, memory, temperature, power, and cost.
`train_resume`	Resume a training job from its latest checkpoint. Rebuilds config with topology revalidation.
`train_snapshot`	Get complete training monitoring snapshot: GPU metrics (utilization, memory, temp, power), training metrics (loss, grad_norm, lr, throughput), straggler detection, and cost estimate.
`train_status`	List all training jobs and their state (created, running, completed, failed).
`train_stop`	Stop a running training job. Kills training processes on all nodes in parallel.
`train_unsloth_run`	Run an Unsloth local model server.
`train_unsloth_start`	Start a coding agent backed by Unsloth's local model server.
`train_unsloth_stop`	Stop a running Unsloth server started with `unsloth run`.
`triggers_create`	Create an event trigger (schedule/webhook/metric).
`triggers_enable`	Enable or disable a trigger.
`triggers_execute`	Manually execute a trigger now.
`triggers_history`	Show trigger execution history.
`triggers_list`	List configured triggers.
`up`	CLI-native provisioning with manifest cache and drift detection. Use --fix-drift to detect and auto-fix drifted infrastructure.
`upsert_database`	Insert or update data in a database table. Performs upsert operation (insert or update on conflict).
`vllm_analyze`	Analyze a running vLLM server's workload and return specific optimization recommendations with before/after comparisons.
`vllm_auto_optimize`	Automatically optimize vLLM configuration by analyzing workload patterns. Selects optimal settings for the 6 critical knobs based on live endpoint metrics or sample request files.
`vllm_benchmark`	Benchmark a vLLM endpoint with concurrent requests. Returns throughput (req/s), success rate, and total latency.
`vllm_inference`	Test inference against a running vLLM endpoint (completions or chat).
`vllm_info`	Get vLLM server info: loaded models, config, and health status.
`vllm_sleep`	Put a vLLM server to sleep. Level 1: offload to CPU (fast wake). Level 2: discard weights (minimal RAM).
`vllm_start`	Start a vLLM inference server on a remote instance via SSH/systemd. Supports Multi-LoRA, Sleep Mode, KV Offloading, Speculative Decoding.
`vllm_stop`	Stop a vLLM server on a remote instance.
`vllm_wake`	Wake a sleeping vLLM server. For Level 2 sleep, also reloads weights and resets prefix cache.
`wandb_create_dashboard`	Create a custom W&B dashboard with GPU metrics, training loss, and cost panels.
`wandb_create_report`	Create a W&B report with custom sections, charts, and narrative text.
`wandb_create_terradev_alerts`	Auto-create standard Terradev alerts: GPU cost > budget, utilization < 50%, training loss spike, straggler detection.
`wandb_create_terradev_dashboard`	Auto-create a Terradev-specific W&B dashboard with GPU utilization, cost tracking, training metrics, and infrastructure panels.
`wandb_create_terradev_report`	Auto-generate a Terradev infrastructure report: GPU costs, provider comparison, training efficiency, and recommendations.
`wandb_dashboard_status`	Get comprehensive W&B monitoring overview: dashboards, reports, alerts, active runs.
`wandb_list_projects`	List all Weights & Biases projects for the configured entity.
`wandb_list_runs`	List runs in a W&B project with status, metrics summary, and config.
`wandb_run_details`	Get detailed info, metrics, and artifacts for a specific W&B run.
`wandb_setup_alerts`	Set up custom W&B alerts for GPU metrics: cost thresholds, utilization drops, training anomalies.
`warm_pool_start`	Start the warm pool manager for intelligent model pre-warming. 5 strategies: traffic_based, time_based, priority_based, cost_optimized, latency_optimized.
`warm_pool_status`	Get warm pool status: hit rate, cold starts, memory saved, cost saved.

Schemas: list_tools.

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Official server.json

{
  "name": "io.github.theoddden/terradev",
  "title": "Terradev",
  "$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
  "remotes": [
    {
      "url": "https://terradev-mcp.terradev.cloud/sse",
      "type": "sse"
    }
  ],
  "version": "6.2.20",
  "packages": [
    {
      "version": "6.2.20",
      "transport": {
        "type": "stdio"
      },
      "identifier": "terradev-cli",
      "runtimeHint": "uvx",
      "registryType": "pypi",
      "registryBaseUrl": "https://pypi.org",
      "packageArguments": [
        {
          "type": "positional",
          "value": "mcp"
        },
        {
          "type": "positional",
          "value": "serve"
        }
      ],
      "environmentVariables": [
        {
          "name": "RUNPOD_API_KEY",
          "isSecret": true,
          "description": "RunPod API key — get at runpod.io/console/settings/api-keys"
        },
        {
          "name": "VAST_API_KEY",
          "isSecret": true,
          "description": "Vast.ai API key — get at console.vast.ai/api-keys"
        },
        {
          "name": "LAMBDA_API_KEY",
          "isSecret": true,
          "description": "Lambda Labs API key — get at cloud.lambdalabs.com/api-keys"
        },
        {
          "name": "HF_TOKEN",
          "isSecret": true,
          "description": "HuggingFace API token — get at huggingface.co/settings/tokens"
        },
        {
          "name": "AWS_ACCESS_KEY_ID",
          "isSecret": true,
          "description": "AWS access key ID (pair with AWS_SECRET_ACCESS_KEY)"
        },
        {
          "name": "AWS_SECRET_ACCESS_KEY",
          "isSecret": true,
          "description": "AWS secret access key"
        },
        {
          "name": "GOOGLE_APPLICATION_CREDENTIALS",
          "description": "Path to GCP service account JSON key file"
        },
        {
          "name": "TERRADEV_MCP_BEARER_TOKEN",
          "isSecret": true,
          "description": "Bearer token for SSE/remote transport mode (stdio mode does not require this)"
        }
      ]
    }
  ],
  "repository": {
    "url": "https://github.com/theoddden/Terradev",
    "source": "github"
  },
  "description": "Cross-cloud GPU orchestration CLI."
}