Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/entropy-data/dataproduct-mcp/claude-mdgit clone --depth 1 https://github.com/entropy-data/dataproduct-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/entropy-data/dataproduct-mcp/claude-md)<a href="https://agentmods.dev/instructions/entropy-data/dataproduct-mcp/claude-md"><img src="https://agentmods.dev/badge/instructions/entropy-data/dataproduct-mcp/claude-md.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.01237 | $0.01237 |
| Opus 5 | $0.00619 | $0.00619 |
| Sonnet 5 | $0.00247 | $0.00247 |
| Haiku 4.5 | $0.00124 | $0.00124 |
Grade A, and why
dataproduct-mcp CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Project Overview
This is a Data Product MCP server that enables AI agents to discover and access data products through the Data Mesh Manager platform. It acts as a bridge between AI systems and data governance infrastructure, allowing agents to find relevant data products, check access permissions, and understand data contracts.
Key Commands
Development Setup
# Install dependencies with dev tools
uv sync --extra dev
uv pip install -e .
Testing
# Run all tests (uses pytest with async support)
uv run pytest
Running the Server
# Start MCP server (uses stdio transport)
uv run python -m dataproduct_mcp.server
# Use with MCP Inspector for debugging
npx @modelcontextprotocol/inspector --config config.json --server dataproduct
Architecture
Core Components
MCP Server (server.py)
- Built with FastMCP framework
- Exposes tools for data product discovery
- Uses stdio transport for MCP communication
- Implements async tools with proper error handling
API Client Layer (datameshmanager/)
datamesh_manager_client.py: Async HTTP client using httpxmodels.py: Pydantic models for API response validation- Handles authentication via
DATAMESH_MANAGER_API_KEYenvironment variable
MCP Tools Architecture
The server exposes these tools to AI agents:
dataproduct_list- List/filter data products by search terms, archetype, statusdataproduct_search- Semantic search for data productsdataproduct_get- Retrieve detailed data product info including access statusdatacontract_get- Get YAML data contract specifications
Data Flow
- Discovery: AI agents use list/search tools to find relevant data products
- Governance: Check access status and permissions via get tools
- Integration: Use returned server information to connect to actual data sources
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 143 lines · 1,237 tokens per session scan A ec067a2626c1
dataproduct-mcp CLAUDE.md is an instructions file published in the GitHub repository entropy-data/dataproduct-mcp (46 stars, last pushed 10mo ago), licensed MIT. It adds 1,237 tokens to every session, about $0.0062 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
blockrun-mcp AGENTS.md
AGENTS.md instructions for BlockRunAI/blockrun-mcp, covering blockrun mcp, commands, project structure, key dependencies and install in codex.
openrouter-mcp-multimodal AGENTS.md
Instructions for stabgan/openrouter-mcp-multimodal, covering agent instructions, before you ship, releasing (read this before publishing), short version and version files (must all match package.json).
intervals-icu-mcp CLAUDE.md
Instructions for hhopke/intervals-icu-mcp, covering claude.md, project overview, development commands, architecture (quick reference) and tool categories.
ai-toolkit AGENTS.md
Instructions for pipefy/ai-toolkit, covering repository guidelines, documentation map, project structure, import namespace migration: pipefysdk → pipefy and src/pipefysdk/init.py (transitional shim).
flyto-core CLAUDE.md
Instructions for flytohub/flyto-core, covering claude notes, cross-agent handoff and shared code intelligence.
Plonk AGENTS.md
Instructions for ostapondo/Plonk, covering agent rules, layout, adding a module, build & verify and code style.