docs2db-mcp-server AGENTS.md

docs2db-mcp-server AGENTS.md is an instructions file for Codex, OpenCode from rhel-lightspeed/docs2db-mcp-server. It costs 1,526 tokens per session, scanned A, original, Apache-2.0.

Repository instructions for a server that gives AI assistants search tools for Red Hat Enterprise Linux documentation. It uses a knowledge-search system backed by a database.

In plain words
What is it for?
Installing dependencies, running code-quality checks, testing with or without external services, type-checking, and starting the documentation search server locally.
Why use it?
They collect the environment setup, checks, tests, and local-run commands needed to work on the documentation search service.

Instructions file for CodexOpenCode

Install

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.

agentmods
npx agentmods add instructions/rhel-lightspeed/docs2db-mcp-server/agents-md
Clone the repo
git clone --depth 1 https://github.com/rhel-lightspeed/docs2db-mcp-server

Made for: Codex, OpenCode.

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README.md
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Per session 1,526 This file is loaded in full into every session.
When invoked 1,526 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.01526 $0.01526
Opus 5 $0.00763 $0.00763
Sonnet 5 $0.00305 $0.00305
Haiku 4.5 $0.00153 $0.00153

Measured 4d ago against content hash 12e14a5308c6, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

docs2db-mcp-server AGENTS.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 4d 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.

AGENTS.md · 188 lines

How it starts

The opening of the file, as written. The whole thing — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md

Knowledge base for AI agents working on this repository.

Project Overview

docs2db-mcp-server is an MCP (Model Context Protocol) server that exposes docs2db-api's RAG engine as tools for AI assistants. It provides semantic and hybrid search capabilities for RHEL documentation.

Stack: FastMCP → docs2db-api (UniversalRAGEngine) → PostgreSQL/pgvector

Related repositories:

Author: Ellis Low ([email protected]) License: Apache-2.0 Python: >=3.12,<3.14 Package manager: uv MCP framework: FastMCP

Development Environment

# Install dependencies
uv sync

# Install pre-commit hooks
uv run pre-commit install

# Run linting/formatting/type checks
uv run pre-commit run --all-files

# CI-safe test run (excludes tests needing external services)
make test-ci

# Run all tests
make test

# Type check
make typecheck

# Run the MCP server locally
export DOCS2DB_MCP_DB_HOST=localhost
export DOCS2DB_MCP_DB_PASSWORD=postgres
uv run python -m docs2db_mcp

Code Architecture

src/docs2db_mcp/
├── __init__.py           # Package metadata
├── __main__.py           # Entry point (python -m docs2db_mcp)
├── config.py             # Pydantic settings (DOCS2DB_MCP_* env vars)
├── engine.py             # Singleton wrapper for UniversalRAGEngine
├── server.py             # FastMCP server instance and @mcp.tool() registrations
└── tools/
    ├── __init__.py
    └── search_documents.py  # MCP tool implementation

Key Patterns and Conventions

  • Logging: structlog (structlog.get_logger()). Do NOT use stdlib logging.
  • Log formatting: Avoid f-strings in structlog calls. Use %s style for lazy evaluation.
  • Config/settings: Pydantic BaseSettings with env_prefix="DOCS2DB_MCP_". Environment variables are the ONLY configuration method. No config files.
  • Imports: Absolute imports only. Example: from docs2db_mcp.config import CONFIG
  • FastMCP imports: from fastmcp import FastMCP, ToolAnnotations
  • docs2db-api imports: from docs2db_api.rag.engine import UniversalRAGEngine
  • Singleton engine: The RAG engine uses a module-level singleton to avoid multiple initializations. See engine.py.
  • Async tools: All MCP tools are async def. Use async def for all I/O operations.
  • ToolAnnotations: Set annotations=ToolAnnotations(readOnlyHint=True) for read-only tools.
  • Tool returns: Return dictionaries (JSON-serializable). Include error handling with graceful degradation.
  • Type hints: Required for all functions and parameters. All modules start with descriptive docstrings.

Read the full file on GitHub · 188 lines

Changes

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.

  1. 4d ago First seen · 188 lines · 1,526 tokens per session scan A 12e14a5308c6

Subscribe to this mod's changes

docs2db-mcp-server AGENTS.md is an instructions file published in the GitHub repository rhel-lightspeed/docs2db-mcp-server (4 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 1,526 tokens to every session, about $0.0076 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 2026-08-31.

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