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8 clients · secrets stay placeholders

JSON (mcpServers)Claude CodeCodexOpenCodeCursorVS CodeGemini CLIGoose

{
  "mcpServers": {
    "ckg-agentforce": {
      "type": "http",
      "url": "https://ckg-agentforce.onrender.com/mcp"
    }
  }
}

claude mcp add --transport http ckg-agentforce https://ckg-agentforce.onrender.com/mcp

[mcp_servers.ckg-agentforce]
url = "https://ckg-agentforce.onrender.com/mcp"

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "ckg-agentforce": {
      "type": "remote",
      "url": "https://ckg-agentforce.onrender.com/mcp",
      "enabled": true
    }
  }
}

{
  "mcpServers": {
    "ckg-agentforce": {
      "type": "http",
      "url": "https://ckg-agentforce.onrender.com/mcp"
    }
  },
  "deeplink": "cursor://anysphere.cursor-deeplink/mcp/install?name=ckg-agentforce&config=eyJ0eXBlIjoiaHR0cCIsInVybCI6Imh0dHBzOi8vY2tnLWFnZW50Zm9yY2Uub25yZW5kZXIuY29tL21jcCJ9"
}

{
  "servers": {
    "ckg-agentforce": {
      "type": "http",
      "url": "https://ckg-agentforce.onrender.com/mcp"
    }
  }
}

{
  "mcpServers": {
    "ckg-agentforce": {
      "httpUrl": "https://ckg-agentforce.onrender.com/mcp"
    }
  }
}

extensions:
  "ckg-agentforce":
    enabled: true
    name: "ckg-agentforce"
    type: streamable_http
    uri: "https://ckg-agentforce.onrender.com/mcp"
    timeout: 300

Quality 80 / 100good

protocol75 · 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
pass
protocol.transportstreamable HTTP
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 hygiene64 · weight 30

pass
tools.descriptionsevery tool has a description
warn
tools.description_length1 tools have descriptions over 1,024 characters, which crowds the context
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: list_concepts (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); get_prerequisites (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); verify_source (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); …
pass
tools.token_costabout 1,673 tokens to load every tool
pass
tools.api_dumptools are not a one-to-one API dump

stability100 · weight 15

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

dependencies100 · weight 15

pass
deps.known_vulnsno known advisory in 30 resolved packages
pass
deps.mcp_sdk_versionmcp 1.30.0, no known advisory
pass
deps.resolvable[email protected] resolved: 30 packages

10 tools, about 1,673 tokens to load them all · tool set 9a2ef26a4741 · checked 2026-10-20 11:40 UTC

Tool changes

No tool definition changes recorded. A server's first observation is its baseline; later probes record what changes.

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 88 / 100

provenance80

liveness90

freshness85

hygiene100

+repository +namespace-matches-repo +reachable +uptime-75% ~updated-83d-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] PyPI29 dependencies, resolved · MCP SDK mcp 1.30.0 · advisories checked 2026-10-19

No known advisory affects this dependency graph.

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://ckg-agentforce.onrender.com/mcp` streamable-http	none	yes	75%	320 ms	2025-11-25	2026-10-20

package	registry	version	runtime	secrets
`ckg-agentforce`	pypi	0.10.3	uvx

Tools (10)

`evaluate_trust_chain`	Return a machine-readable JSON trust chain + ROI analysis + payment recommendation. Designed for autonomous agent consumption. The agent reads the JSON, evaluates the ROI gate, selects the cheapest positive-ROI payment option, and purchases autonomously if budget is pre-authorized. Trust chain closes the audit gap that RAG cannot: source_hashes → SHA-256 of source docs (what was known at extraction time) graph_hash → SHA-256 of the installed graph file (what is declared now) signer_key_id → Ed25519 public key fingerprint (who is attesting) benchmark_receipt → BoundaryAttest-signed F1 claim (cryptographic performance proof) scitt_status → SCITT ledger submission status (public audit anchor) ROI gate: if total_value_per_call_usd > payment_option.price_per_call_usd → autonomous_pay = true. Returns: JSON string (schema: ckg-trust-chain-v1).
`export_benchmark_receipt`	Generate a signed BoundaryAttest receipt for the agentforce CKG benchmark result. Produces a `ckg.benchmark_result.exported` claim signed with this server's Ed25519 session key. Any party holding the public key can verify the signature over the canonical JSON claim. Implements GuardrailDecisionV1 · experimental-interop-v0.1. Result: F1 0.471 over 30 queries · ckg-benchmark v0.6.2 · 4× over RAG baseline (0.123).
`get_prerequisites`	Return the full ordered prerequisite chain for an AgentForce concept. Shows everything the concept depends on — the complete upstream path. Args: concept: Target concept — e.g. 'Autonomous Resolution', 'Multi-LoRA Serving', 'Custom Actions', 'Semantic Retrieval'.
`list_concepts`	List all 40 AgentForce concepts in this knowledge graph.
`query_ckg`	Traverse the AgentForce knowledge graph from any concept. Returns prerequisites (what this concept needs) and dependents (what it enables). Every relationship traces to an authoritative Salesforce doc URL. Args: concept: Concept name — e.g. 'Autonomous Resolution', 'Einstein Trust Layer', 'Service Agent', 'Grounding', 'NVIDIA NIM'. depth: Traversal depth 1–5 (default 3).
`query_intersect`	Answer a conjunctive query: concepts reachable from EVERY anchor at once (A AND B). query_ckg walks outward from one concept. This intersects the reachable sets of two or more, which is the shape of most real questions — "the component that satisfies A AND applies to B". Neither anchor alone answers it; the answer lives in the overlap. Every branch is an exact set of declared edges, so the intersection is exact. A concept appears only if a declared path reaches it from each anchor. A relation missing from the graph produces an empty result, never a guess. Args: branches: Two or more branches. Either a bare anchor ("TensorRT-LLM"), which takes everything within `depth` hops, or an anchor plus an explicit relation path using '>' ("TensorRT-LLM > REQUIRES > ENABLES"), where each relation replaces the frontier. '*' matches any relation. Mix both forms freely. depth: Hops for bare-anchor branches, 1-5 (default 2). Ignored for explicit paths. direction: 'out' follows dependencies, 'in' follows them backwards, 'both' (default). mode: 'AND' (default) intersects branches; 'OR' unions them. limit: Max concepts listed, 1-200 (default 40). The true count is always shown. Returns: Markdown with the query plan and its per-step set sizes, then the answer set with taxonomy tags. Reports which branch was empty when the intersection is empty.
`resolution_path`	Trace the exact path that determines an AgentForce autonomous resolution event. This is the $2/resolution billing path — what the agent must traverse correctly to resolve autonomously without human handoff.
`route_query`	Route an AgentForce question to the optimal model and reasoning approach via graph depth. The CKG graph IS the router. AgentForce dependency chains (e.g. Einstein Trust Layer → Data Cloud → NVIDIA NIM → Resolution Criteria) have typed hops that signal reasoning complexity deterministically. No heuristic: the graph decides. Routing table: hop_depth 1 → haiku · direct (simple concept lookup) hop_depth 2 → sonnet · generic_cot (moderate chain) hop_depth 3+ → opus · sparql_cot (deep dependency, structured reasoning) Args: question: Concept name or natural language question about Salesforce AgentForce. Returns: model_tier + reasoning_approach + why + context subgraph to inject before LLM call.
`search_concepts`	Find AgentForce concepts by keyword. Args: query: Search term — e.g. 'resolution', 'trust', 'grounding', 'action', 'NIM'.
`verify_source`	Return the source URL and content hash for an AgentForce concept node. Audit chain: edge answer → graph commit → source_hash → source_url (fetch hint) Verification: curl -s <source_url> \| sha256sum # compare output to source_hash Args: concept: Concept label (partial match supported). receipt: If True, also return a signed BoundaryAttest receipt envelope (experimental-interop-v0.1) binding concept_label + source_url + source_hash + timestamp to this server's ed25519 session key.

Schemas: list_tools.

Similar MCP servers

More from io.github.Yarmoluk

Official server.json

{
  "name": "io.github.Yarmoluk/ckg-agentforce",
  "title": "Salesforce AgentForce CKG",
  "$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
  "remotes": [
    {
      "url": "https://ckg-agentforce.onrender.com/mcp",
      "type": "streamable-http"
    }
  ],
  "version": "0.10.3",
  "packages": [
    {
      "version": "0.10.3",
      "transport": {
        "type": "stdio"
      },
      "identifier": "ckg-agentforce",
      "runtimeHint": "uvx",
      "registryType": "pypi"
    }
  ],
  "repository": {
    "url": "https://github.com/Yarmoluk/ckg-agentforce",
    "source": "github"
  },
  "description": "Salesforce AgentForce knowledge graph — 40 nodes, Einstein Trust Layer, 11x fewer tokens than RAG."
}