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mcp

Video scene understanding for AI agents via the Primate Vision API.

Primate-Intelligence0 stars0 forksAI & Agents
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Install

Terminal

$npx -y @primate-intelligence/mcp

mcp_config.json

{
  "mcpServers": {
    "ai-primateintelligence-mcp": {
      "env": {
        "PRIMATE_API_KEY": "${PRIMATE_API_KEY}"
      },
      "args": [
        "-y",
        "@primate-intelligence/mcp"
      ],
      "command": "npx"
    }
  }
}

Documentation

@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

ToolDoesRead-only
create_video_from_urlRegister a video from a public https URL (POST /v1/videos)—
create_analysisAsk a question about a video (POST /v1/analyses)—
validate_analysisDry-run a prompt: assessability + cost estimate, zero credits (validate_only: true)✓
create_analysis_batch2–10 prompts on one video; each after the first billed at 50% (POST /v1/analyses/batch)—
get_analysisFetch analysis status/result (GET /v1/analyses/{id})✓
wait_for_analysisPoll until terminal state; returns { analysis, retry }✓
list_modelsList available models (GET /v1/models)✓
get_usageCredit balance + period meters (GET /v1/usage)✓
get_creditsBalance + per-analysis transaction ledger (GET /v1/credits)✓
get_test_fixtureStable fixture for integration self-verification (GET /v1/test-fixture)✓

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. get_test_fixture → verify the integration works (test keys return deterministic results, no quota burn)
  2. create_video_from_url with the video URL
  3. validate_analysis → confirm the prompt is assessable + preview estimated_cost_usd (free)
  4. create_analysis with the question — "Is there a person in this video?" — or create_analysis_batch for several
  5. wait_for_analysis → result.answer (yes | no | indeterminate) + result.confidence + result.clips + result.detected_count (count queries) + result.indeterminate_reason
  6. On insufficient_credits: call get_credits, report the balance + recent debits, point the human at billing

Security contract

The API key is read from the PRIMATE_API_KEY environment variable only. No tool accepts a key, token, or secret as an argument — so credentials never land in agent transcripts, tool-call logs, or model context. This is enforced by a unit test that fails the build if any tool schema grows a credential-shaped parameter.

Errors surface the machine-readable error code, a docs_url, and the request_id so an agent can self-correct without a human in the loop.

Configuration

VarRequiredDefault
PRIMATE_API_KEYyes—
PRIMATE_BASE_URLnohttps://api.primateintelligence.ai

Development

npm install
npm test        # vitest — tool surface, security contract, polling, error shape
npm run build   # tsc → dist/

Links

License

MIT © Primate AI, Inc.

Sourced from the repository README.

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