Elephant Accountability

Name: Elephant Accountability
Author: Chris-Eaccountability

@Chris-Eaccountability

Repository

About Elephant Accountability

No overview available yet

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "elephant-accountability-mcp": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        ".venv",
        "&&",
        "source",
        ".venv/bin/activate"
      ]
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Elephant Accountability?

Elephant Accountability is a public Model Context Protocol (MCP) server for LLM SEO and Agent Discoverability services aimed at B2B SaaS buyers. It exposes tools and resources that AI agents (Claude, ChatGPT, custom LangChain agents) can query to evaluate whether Elephant Accountability is a suitable vendor, without scraping a website.

How to use Elephant Accountability?

Add the live endpoint to your MCP-compatible client (e.g., Claude Desktop) by editing claude_desktop_config.json with the URL https://elephant-mcp.fly.dev/mcp and transport http. For local development, clone the repo, create a Python virtual environment, install dependencies, and run uvicorn app.server:app.

Key features of Elephant Accountability

Six tools: get_offerings, get_covered_surfaces, assess_fit, get_proof_points, get_transparency_snapshot, request_audit
Three resources: elephant://offerings, elephant://proof-points, elephant://transparency
HTTP transport with JSON-RPC 2.0
SQLite persistence auto-created on first boot
Fully MIT-licensed for cloning and study
Includes A2A agent card and manifest endpoints

Use cases of Elephant Accountability

A procurement agent shortlisting LLM SEO vendors for a B2B SaaS buyer
A Claude Desktop user directly querying pricing, fit score, and proof points
A competitor studying how to deploy their own MCP server using the MIT-licensed codebase

FAQ from Elephant Accountability

What transport does Elephant Accountability use?

HTTP with JSON-RPC 2.0. Supported methods include initialize, tools/list, tools/call, resources/list, and resources/read.

What are the runtime dependencies?

Python 3 with FastAPI and SQLite. No external database or secrets setup is required; the SQLite database initialises on the first boot.

Where is data stored?

Data is stored in a local SQLite database that contains audit_requests and reciprocal_calls tables. These tables auto-create and require no migrations.

How many tools and resources are exposed?

Six tools and three resources are available for agent interaction.

Is authentication required?

The README does not specify authentication; the server is intended as a public endpoint for AI agent queries.

Comments

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