LLM SEO MCP — Elephant Accountability
LLM SEO and Agent Discoverability for B2B SaaS. Pricing, fit assessment, audit requests.
Open source Repository Open in the app JSON README (API)
About
LLM SEO and Agent Discoverability for B2B SaaS. Pricing, fit assessment, audit requests.
Details
- Kind
- MCP servers
- Topic
- AI, RAG & memory
- Publisher
- chris-eaccountability
- Origin
- official
- Category
- ferramentas
- Transport
- http
- Version
- 1.0.1
- Last push
- 2026-05-05T17:32:07Z
- Repository state
- ativo
- Language
- Python
- License
- MIT
- Added
- 2026-08-29 03:01:47
- Updated
- 2026-08-29 03:01:47
- Origin id
io.github.Chris-Eaccountability/elephant-accountability-mcp
README
# 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
```bash
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:
```json
{
"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)
```bash
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
```bash
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](./LICENSE).
## Publisher
**Elephant Accountability LLC**
Christopher Kenney, sole member / manager
United States
chris@eaccountability.org