What is local-docs-mcp AGENTS.md?

Project instructions for a local-document search system. The system indexes documents, turns their meaning into searchable data, stores it in Qdrant, and lets AI assistants search it through MCP.

Use it when working on document indexing, semantic search, the MCP server, or tests in the local-docs-mcp project.

It gives an AI coding agent the project's layout, commands, and operating model in one place. This reduces guesswork when indexing documents, starting the server, or running tests.

How nguyenchiencong/local-docs-mcp configures Codex and OpenCode

Reuse

Borrowing it

Nothing to install: this file belongs to nguyenchiencong/local-docs-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/nguyenchiencong/local-docs-mcp/main/AGENTS.md

Clone the repo

git clone --depth 1 https://github.com/nguyenchiencong/local-docs-mcp

Made for: Codex, OpenCode.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/instructions/nguyenchiencong/local-docs-mcp/agents-md/github.svg)](https://agentmods.dev/instructions/nguyenchiencong/local-docs-mcp/agents-md)

Your own site

<a href="https://agentmods.dev/instructions/nguyenchiencong/local-docs-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/nguyenchiencong/local-docs-mcp/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/instructions/nguyenchiencong/local-docs-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/nguyenchiencong/local-docs-mcp/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 733 This file is loaded in full into every session.

When invoked 733 The same file — it is already loaded in full.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

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.

Model	Per session	Once invoked
Fable 5.1	$0.00733	$0.00733
Opus 5.5	$0.00293	$0.00293
Sonnet 5.5	$0.00147	$0.00147
Haiku 4.5	$0.00073	$0.00073

Measured 2mo ago against content hash b041cda3b3b0, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

local-docs-mcp 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 2mo 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 · 102 lines

How it starts

The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Project Overview

This is a modular semantic search system with MCP (Model Context Protocol) integration for indexing and searching local documentation using Qdrant vector database and Ollama embeddings. The system provides AI assistants with semantic search capabilities through the Model Context Protocol.

Key Commands

Indexing Documents

# Index documents from the docs/ directory
uv run python -m src.indexing.main_flow

# Alternative using the project script
local-docs-index

Running the MCP Server

# Start the MCP server for AI assistant integration
uv run python -m src.mcp_server.server

# Alternative using the project script
local-docs-mcp

Testing

# Run tests with pytest directly (if available)
uv run pytest tests/

Architecture Overview

Core Components

Indexing Module (src/indexing/)
- main_flow.py: Main CocoIndex flow for document processing and embedding generation
- chunking.py: Text chunking utilities using Chonkie library
Search Service (src/search/)
- service.py: Decoupled semantic search service with various search strategies
- models.py: Data models for search results and configuration
MCP Server (src/mcp/)
- server.py: MCP server implementation exposing search tools
- tools.py: MCP tool definitions and handlers

Configuration

Environment Variables: Set in .env file (copy from .env.example)
Project Settings: pyproject.toml contains dependencies and build configuration

Key Dependencies

cocoindex: Document processing and indexing framework
qdrant-client: Vector database client
ollama: Local AI model serving
mcp: Model Context Protocol library
chonkie: Text chunking library

Development Notes

Document Indexing

Documents are sourced from the docs/ directory by default
Text is chunked using Chonkie library before embedding
Embeddings are generated using Ollama with the specified model
Results are stored in Qdrant collection named "local-docs-collection"

Read the full file on GitHub · 102 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.

2mo ago First seen · 102 lines · 733 tokens per session scan A b041cda3b3b0

Subscribe to this mod's changes

local-docs-mcp AGENTS.md is an instructions file published in the GitHub repository nguyenchiencong/local-docs-mcp (2 stars, last pushed 11mo ago), licensed MIT. It adds 733 tokens to every session, about $0.0029 per session on Opus 5.5. 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.

Data and AI Documents RAG and vectors

local-docs-mcp AGENTS.md next to 6 instructions from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
local-docs-mcp AGENTS.mdnguyenchiencong/local-docs-mcp	Instructions file	2	11mo ago	MIT	A	733	Codex, OpenCode
vscode buildNext.instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	9,719	GitHub Copilot
next.js AGENTS.mdvercel/next.js	Instructions file	143k	yesterday	MIT	A	7,461	Codex, OpenCode
langchain AGENTS.mdlangchain-ai/langchain	Instructions file	149k	yesterday	MIT	A	4,680	Codex, OpenCode
vscode oss-third-party-notices.instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	5,516	GitHub Copilot
vscode copilot-instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	3,093	GitHub Copilot
deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness	Instructions file	274k	2d ago	MIT	A	4,313	Codex, OpenCode

local-docs-mcp: Instructions file for Codex