Primateintelligence MCP server

@primate-intelligence/mcp

MCP (Model Context Protocol) server for the Primate Vision video analysis API — a video understanding API by Primate Intelligence (docs · llms.txt).

Gives AI agents video scene understanding as tools: register a video, ask a question in plain English, get a deterministic answer with a confidence score and clip timestamps. No hallucinated descriptions — the answer is yes / no / indeterminate with evidence.

Try it for free

A free test key requires no email, no card, no signup:

curl -X POST https://api.primateintelligence.ai/v1/sandbox

Your AI agent can do this for you — right from Claude. Point it at primateintelligence.ai/llms.txt and it can discover, provision, integrate, and self-verify with zero human steps.

Two ways to connect

1. Remote server (recommended) — OAuth, nothing to install

Streamable HTTP endpoint with full OAuth 2.1 + Dynamic Client Registration + PKCE:

https://api.primateintelligence.ai/mcp

In Claude.ai / Claude Desktop: Settings → Connectors → Add custom connector, paste the URL, sign in. No API key handling — the OAuth flow issues and rotates tokens for you.

2. Local stdio server

// claude_desktop_config.json · .mcp.json · mcp.json · .cursor/mcp.json
{
  "mcpServers": {
    "primate-intelligence": {
      "command": "npx",
      "args": ["-y", "@primate-intelligence/mcp"],
      "env": { "PRIMATE_API_KEY": "pv_live_…" }
    }
  }
}

Tools

Every tool carries MCP annotations (title, readOnlyHint, destructiveHint, idempotentHint, openWorldHint), declares an outputSchema, and returns structuredContent conforming to it. No tool deletes data. Tool descriptions and schemas mirror the OpenAPI document at GET /v1/openapi.json — the spec is the source of truth.

Typical agent flow

1. gettestfixture → verify the integration works (test keys return deterministic results, no quota burn)
2. createvideofrom_url with the video URL
3. validateanalysis → confirm the prompt is assessable + preview estimatedcost_usd (free)
4. createanalysis with the question — "Is there a person in this video?" — or createanalysis_batch for several
5. waitforanalysis → result.answer (yes | no | indeterminate) + result.confidence + result.clips + result.detectedcount (count queries) + result.indeterminatereason
6. On insufficientcredits: call getcredits, report the balance + recent debits, point the human at billing

Security contract

Shortened. The full README is on GitHub.