# ai.jeda/jeda-ai

> Visual AI for strategic thinking — SWOT, flowcharts, mindmaps, Gantt diagrams as polished SVG.

- name: ai.jeda/jeda-ai
- version: 1.23.40
- connection class: R0 (autonomous)
- trust: 76/100 flags: no-repository
- quality: 85/100 (good)
- owner: not claimed
- descriptor: https://api.protogrid.dev/v1/servers/ai.jeda%2Fjeda-ai
- tools: https://api.protogrid.dev/v1/servers/ai.jeda%2Fjeda-ai/tools

**An agent can connect right now, no human step.** Remote endpoint, no authentication, reachable on the last probe.

## Connection (mcpServers)

```json
{
  "mcpServers": {
    "jeda-ai": {
      "type": "http",
      "url": "https://mcp.jeda.ai/mcp"
    }
  }
}
```


## Trust

- hygiene: 80
- liveness: 100
- freshness: 65
- provenance: 55
- drivers: +dns-namespace +website +reachable +uptime-100% ~updated-178d-ago -no-repository
- Derived from observable signals (official registry feed and our own credential-free probes); no code audit performed.

## Quality 85/100 (good)

### protocol: 75 (weight 25)

- warn `protocol.modern`: newest supported version is 2025-11-25; 2026-07-28 not supported, older versions are deprecated until 2027-07-28
- n/a `protocol.stateless`: only applies to 2026-07-28 servers
- pass `protocol.transport`: streamable HTTP
- n/a `protocol.list_ttl`: only applies to 2026-07-28 servers

### authorization: n/a (weight 15)

- n/a `auth.prm`, `auth.as_metadata`, `auth.cimd`: no remote uses OAuth
- n/a `auth.secret_in_url`: no templated URL

### tool hygiene: 86 (weight 30)

- pass `tools.descriptions`: every tool has a description
- warn `tools.description_length`: 2 tools have descriptions over 1,024 characters, which crowds the context
- pass `tools.schemas`: every tool has an object input schema
- pass `tools.annotations`: 5 of 5 tools declare readOnlyHint or destructiveHint
- pass `tools.directory_hints`: every tool declares readOnlyHint, destructiveHint, idempotentHint and openWorldHint
- warn `tools.token_cost`: about 18,730 tokens to load every tool
- pass `tools.api_dump`: tools are not a one-to-one API dump

### stability: 100 (weight 15)

- pass `stability.changes`: no tool changes in 30 days
- pass `stability.rug_pull`: no tool changed its meaning under the same name

### dependencies: n/a (weight 15)

- n/a `deps.known_vulns`, `deps.mcp_sdk_version`, `deps.resolvable`: no npm or PyPI package

- tools: 5, about 18,730 tokens to load them all
- tool set hash: c5d687a599a51970a1d10abec7af1158
- badge: [![protogrid quality](https://protogrid.dev/badge/ai.jeda/jeda-ai.svg)](https://protogrid.dev/servers/ai.jeda/jeda-ai)
- Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed.

### Tool changes

_No tool definition changes recorded._

## Remotes

- https://mcp.jeda.ai/mcp (streamable-http, auth none, reachable true, uptime30d 1)

## Packages

_none_

## Tools (5)

- `generate_diagram`: DEPRECATED alias for `generate_visual` (removed no earlier than v1.24.x). Use `generate_visual` going forward — identical behaviour.
- `generate_framework_analysis`: Runs a specific framework against a user's inputs and produces a full analysis: prose explanation + structured fields + a self-contained SVG visualisation. Powered by the Jeda.ai · Visual AI framework knowledge graph.

ALWAYS call `recommend_framework` FIRST to obtain a valid framework_id -- it returns the exact namespaced slug to pass here. Framework ids are namespaced by category (e.g. SWOT = "cms_swot-analysis", Porter's 5 Forces = "cms_porters-five-forces"); a bare slug like "swot-analysis" will 404.

REQUIRED post-processing on success: the response's first text block contains a `curl -fsSL` save-directive for the SVG. Save the file locally and surface it as a file chip — do NOT summarise the visual in prose.

Input: framework_id (namespaced slug from `recommend_framework`) + user_inputs object (per-framework field set), OR a free-text `raw_prompt` fallback.

Example: { "framework_id": "cms_swot-analysis", "user_inputs": { "subject": "Q3 EU market entry", "context": "B2B SaaS, $5M ARR, US-headquartered" } }
- `generate_visual`: Generates a visual (flowchart, mindmap, matrix, timeline, Gantt, ER, sequence, swimlane, block diagram) as a self-contained SVG. Powered by Jeda.ai · Visual AI.

REQUIRED post-processing on every success: the response's FIRST text block is a "SAVE THIS NOW" directive with a `curl -fsSL` one-liner (Step 1) and an inline-SVG fallback (Step 2). Always attempt Step 1 first; only fall back to Step 2 if curl exits non-zero. The user expects an "Image · SVG" file chip — not a markdown link or prose summary. The signed URL is safe to fetch (auth is in the query string).

Two modes: (1) natural-language `prompt` → Data Agent + Design Agent build the visual; (2) structured `nodes` + `edges` for precise control.

Visual types: matrix | mindmap | flowchart | block-diagram | timeline | gantt | er-diagram | sequence | process-flow. Default: auto-detected from prompt. Slash commands work too: `/matrix SWOT`, `/timeline roadmap`, `/process-flow procurement approval with finance and legal lanes`.

For style options (themes, fonts, arrow styles, animations, per-edge overrides, matrix/process-flow specifics) call `get_style_options` first. Default theme is `pastel` — only override when the user explicitly names another.

Example: { "prompt": "user signup with email verification", "theme": { "preset": "pastel" } }
- `get_style_options`: Returns the full style-options catalog for `generate_visual` — theme presets, fonts, arrow styles, line styles, arrowheads, scene/connector animations, layout directions, per-edge overrides, and matrix/process-flow specifics. Call this once when you need to expose specific style choices to the user (e.g. "what themes are available?") or before composing a `generate_visual` call that needs non-default styling. Output is plain text, ~4 KB.
- `recommend_framework`: Recommends business / strategy / risk frameworks for a stated problem. Powered by the Jeda.ai · Visual AI framework knowledge graph (~2,100 frameworks across 19 categories, edge-curated).

Use when the user describes a business problem ("customer churn rising", "evaluating market entry", "need to assess vendor risk") rather than naming a specific framework. Returns top-N frameworks ranked by fit, each with a concrete reason citing the specific problem signals matched.

Input: just the problem statement is enough. Optional faceted filters (`persona`, `regulation`, `decision_stage`) narrow the candidate set. Set `limit` between 3 and 10 for picker UIs.

Pair with `generate_framework_analysis` to actually run a recommended framework against the user's inputs.

Example: { "problem_statement": "We need to decide whether to enter the EU SMB market in Q3", "decision_stage": "decide", "limit": 5 }