elephant-accountability-mcp

elephant-accountability-mcp

MCP server that exposes Elephant Accountability's audit tiers, EVI v0.9 methodology, fit assessment, proof points, and agent-initiated audit requests to AI agents.

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Elephant Accountability MCP Server

MCP server for the Elephant Accountability certification bureau. Surfaces vendor offerings, methodology, and audit requests to AI agents over the Model Context Protocol.

Live endpoint: https://elephant-mcp.fly.dev/mcp Manifest: https://elephant-mcp.fly.dev/.well-known/mcp.json A2A Agent Card: https://elephant-mcp.fly.dev/.well-known/agent.json Homepage: https://eaccountability.org

What this server does

When an AI agent (Claude, ChatGPT, a custom LangChain agent, etc.) needs to evaluate Elephant Accountability against the EVI v0.9 standard or surface its audit tiers to a buyer, it queries this server instead of scraping a website.

Six tools are exposed:

Tool Purpose
get_offerings Audit and engagement tiers (self-serve, done-for-you, retainer) with delivery SLAs and pointers to /get-started for canonical pricing
get_covered_surfaces The agent-discoverable surfaces measured by EVI v0.9: llms.txt, Schema.org, MCP, A2A, .well-known/agent.json, UCP, structured pricing, directory registrations
assess_fit 0–100 fit score for a buyer's company across stage, vertical, AI features, platform partnerships
get_proof_points Live client outcomes with metrics (includes related-party disclosures)
get_transparency_snapshot Weekly LLM visibility measurements across 5 LLMs
request_audit Agent-initiated audit requests; routed to Stripe, Calendly, or email triage

Two resources are exposed via resources/list: elephant://offerings, elephant://proof-points, elephant://transparency.

Why you'd use this

  • You're building a procurement agent and want to query Elephant Accountability's audit tiers and EVI v0.9 methodology directly.
  • You're at Claude Desktop / Cursor / any MCP-compatible client and want direct access to Elephant's offerings + fit assessment.
  • You're a competitor studying how to deploy your own MCP server — this repo is MIT-licensed, clone freely.

Quickstart — local development

git clone https://github.com/Chris-Eaccountability/elephant-accountability-mcp.git
cd elephant-accountability-mcp

python -m venv .venv && source .venv/bin/activate
pip install -r requirements-dev.txt

# Run the server
uvicorn app.server:app --reload --host 0.0.0.0 --port 8080

# In another terminal, hit it
curl http://localhost:8080/.well-known/mcp.json
curl -X POST -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0", "id":1, "method":"tools/list"}' \
  http://localhost:8080/mcp

Quickstart — add to Claude Desktop

Edit claude_desktop_config.json and add:

{
  "mcpServers": {
    "elephant-accountability": {
      "url": "https://elephant-mcp.fly.dev/mcp",
      "transport": "http"
    }
  }
}

Restart Claude Desktop. Ask: "Is Elephant Accountability a good fit for a seed-stage AEC SaaS that ships AI features?" — Claude will call assess_fit and give a scored answer.

Deploy your own copy (Fly.io)

fly launch --name your-mcp-name --region iad --no-deploy
fly volumes create elephant_mcp_data --size 1 --region iad
fly deploy

That's it. No secrets, no database setup — the server initializes its SQLite DB on first boot.

Architecture

Single FastAPI app. Three files do real work:

app/
├── server.py      # FastAPI routes, JSON-RPC dispatch, SQLite persistence
├── content.py     # Source-of-truth content: manifest, offerings, proof points
└── __init__.py    # Version

Storage:

  • audit_requests table — every agent-initiated audit request, persisted for follow-up
  • reciprocal_calls table — tracks which AI clients have called which tools (buyer-intent signal)

Both tables auto-create on first boot. No migrations.

Running tests

pip install -r requirements-dev.txt
pytest -v

21 tests cover manifest, A2A card, JSON-RPC dispatch, each tool handler, persistence, and CORS.

Protocol compliance

  • MCP version: 2024-11-05
  • Transport: HTTP with JSON-RPC 2.0
  • Methods supported: initialize, tools/list, tools/call, resources/list, resources/read

Contributing

This repo is the canonical source of truth for what Elephant Accountability exposes to AI agents. PRs welcome for:

  • Protocol updates (MCP spec changes)
  • New tool shapes that agents find useful
  • Bug fixes

For service inquiries or content changes (proof points, methodology), email chris@eaccountability.org rather than opening a PR.

License

MIT. See LICENSE.

Publisher

Elephant Accountability LLC Christopher Kenney, sole member / manager United States chris@eaccountability.org

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