Elephant Accountability
RemoteAbout Elephant Accountability
An MCP server for Elephant Accountability’s certification and audit services, aimed at procurement agents and buyers evaluating its offerings. It provides structured information on services, methodology, fit, outcomes, and audit requests.
Key features
- 01Query audit and engagement tiers
- 02Review EVI v0.9 coverage surfaces
- 03Score buyer-company fit
- 04Retrieve client proof points
- 05View LLM visibility measurements
- 06Submit audit requests
Use cases
- 01Compare audit tiers during procurement
- 02Assess fit for a prospective buyer
- 03Review visibility measurements and outcomes
- 04Request an audit through an agent
MCP support
It exposes six tools and three resources to MCP clients over HTTP, covering offerings, methodology, fit assessments, proof points, transparency data, and audit requests.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://elephant-mcp.fly.dev/mcpclaude mcp add --transport http elephant-accountability https://elephant-mcp.fly.dev/mcp{"mcpServers":{"elephant-accountability":{"type":"http","url":"https://elephant-mcp.fly.dev/mcp"}}}Open in Cursor{"servers":{"elephant-accountability":{"type":"http","url":"https://elephant-mcp.fly.dev/mcp"}}}Open in VS Code{"mcpServers":{"elephant-accountability":{"type":"http","url":"https://elephant-mcp.fly.dev/mcp"}}}{"mcpServers":{"elephant-accountability":{"serverUrl":"https://elephant-mcp.fly.dev/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
Details
From the project READMEElephant 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_requeststable — every agent-initiated audit request, persisted for follow-upreciprocal_callstable — 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 [email protected] rather than opening a PR.
License
MIT. See LICENSE.
Publisher
Elephant Accountability LLC Christopher Kenney, sole member / manager United States [email protected]
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.