LLM SEO MCP — Elephant Accountability
io.github.Chris-Eaccountability/elephant-accountability-mcp
LLM SEO and Agent Discoverability for B2B SaaS. Pricing, fit assessment, audit requests.
LLM SEO MCP — Elephant Accountability
io.github.Chris-Eaccountability/elephant-accountability-mcp
LLM SEO and Agent Discoverability for B2B SaaS. Pricing, fit assessment, audit requests.
LLM SEO MCP — Elephant Accountability · v1.0.1
by Chris-Eaccountability
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_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 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