How do I use Local File Search MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Local File Search MCP find all PDF files in the zoology samples" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Local File Search MCP

by dchatpar

Overview Schema Related Servers Score Discussions

Python

Hybrid

Local File Search MCP Agent

LangChain CLI agent that combines an in-process Local File Search MCP (FastMCP) with the remote Microsoft Learn MCP. The LLM uses MiniMax via the OpenAI-compatible API.

Features

search_files — metadata filters (name, folder, extension, dates, size)
search_pdf_content — PDF full-text keyword search via pypdf
Microsoft Learn MCP at https://learn.microsoft.com/api/mcp (streamable_http)
SKILL-based routing with JSON-only local results and 2000-char MS answers
Async REPL CLI

Documentation

Guide	Description
docs/README.md	Documentation index
docs/PROJECT_OVERVIEW.md	Architecture and what was built
docs/LLM_PROVIDER_GUIDE.md	MiniMax ↔ OpenAI migration
docs/DEPLOYMENT.md	Deploy: local, GitHub, Docker, systemd
docs/OPERATIONS.md	Operations and CI
docs/TROUBLESHOOTING.md	Common issues
docs/COMPLIANCE_REPORT.md	Assignment audit
docs/INSTRUCTIONS_FOR_ABIN.md	Reviewer guide for `install.py`

Setup

One command (recommended):

git clone https://github.com/dchatpar/mcp-file-agent.git
cd mcp-file-agent
chmod +x install.py
./install.py --non-interactive --skip-e2e   # no API key; full gate without E2E
# Or interactive: ./install.py

Manual setup:

cd mcp-file-agent
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"
cp .env.example .env
# Set OPENAI_API_KEY in .env (never commit .env)
python scripts/generate_samples.py

MiniMax (OpenAI-compatible)

Configure .env:

OPENAI_API_KEY=<your MiniMax API key>
OPENAI_BASE_URL=https://api.minimax.io/v1
OPENAI_MODEL=MiniMax-M2.7

LangChain uses ChatOpenAI with base_url pointing at MiniMax. MiniMax-only extra_body (thinking disabled) is applied automatically when the base URL contains minimax. OPENAI_API_BASE_URL is accepted as an alias for OPENAI_BASE_URL.

Assignment / OpenAI GPT-5.x: Defaults to MiniMax-M2.7. For OpenAI, copy .env.openai.example to .env or set OPENAI_BASE_URL=https://api.openai.com/v1, your GPT model id, and an OpenAI API key. Full steps: docs/LLM_PROVIDER_GUIDE.md.

Environment

Variable	Default	Description
`OPENAI_API_KEY`	—	Required for agent E2E (MiniMax key)
`OPENAI_BASE_URL`	`https://api.minimax.io/v1`	MiniMax OpenAI-compatible endpoint
`OPENAI_MODEL`	`MiniMax-M2.7`	Model name on MiniMax
`SEARCH_ROOT`	`data/samples/zoology`	Sandboxed search directory
`FILE_SEARCH_ROOT`	(same as `SEARCH_ROOT`)	Alias for `SEARCH_ROOT`
`MICROSOFT_LEARN_MCP_URL`	`https://learn.microsoft.com/api/mcp`	Learn MCP endpoint
`MS_ANSWER_MAX_CHARS`	`2000`	Max length for Microsoft Learn answers

Run CLI

file-search-agent
# or
python -m file_search_agent.main

Sample queries

Local (JSON only): What PDF files are available in our system?
Learn (≤2000 chars): What is Azure Blob Storage?
PDF content search: Find mentions of migration in the PDFs
Out-of-scope: What is the capital of France? → refusal JSON

Test data

The data/samples/zoology/ directory holds 8 non-technical zoology files used for all local-search tests:

File	Extension	Description
`african_elephant_study.pdf`	.pdf	Elephant population dynamics
`marine_mammals_report.pdf`	.pdf	Orca/dolphin hydrophone survey
`bird_migration_analysis.pdf`	.pdf	Arctic tern geolocator study
`amphibian_survey_2023.pdf`	.pdf	Chytrid fungus impact assessment
`coral_reef_observations.docx`	.docx	Great Barrier Reef transect notes
`species_count_2024.xls`	.xls	Endangered species population counts
`field_notes_borneo.txt`	.txt	Borneo rainforest expedition diary
`jaguar_photo_rainforest.jpg`	.jpg	Camera-trap image placeholder

Regenerate with: python scripts/generate_samples.py

Verification

QA matrix

Check	Command	API key	Expected
Lint	`ruff check src tests scripts install.py`	No	All checks passed
Unit tests	`pytest -v`	No	40 passed
E2E agent	`python -u scripts/e2e_verify.py`	Yes	5/5 PASSED (~1–2 min)
Production gate	`python -u scripts/production_gate.py`	Yes	All 6 steps PASS (~90s)
Sample data	`python scripts/generate_samples.py`	No	8 files in `data/samples/zoology/`

Run lint and unit tests in parallel:

source .venv/bin/activate
pip install -e ".[dev]"
python scripts/generate_samples.py
ruff check src tests scripts & pytest -v & wait

E2E (requires OPENAI_API_KEY in .env):

Takes about 1–2 minutes. Use unbuffered output so progress prints appear immediately ([1/5] … [5/5]):

python -u scripts/e2e_verify.py

Checks:

PDF files query → local tools, JSON with PDF entries
List all files → local tools, 8 files total
Elephant search → local tools, elephant match in JSON
Azure Blob Storage → Learn MCP only, answer ≤ 2000 chars
Out-of-scope (capital of France) → assignment error JSON, no tools

Interactive CLI smoke test:

file-search-agent

Assignment compliance

Requirement	Implementation	Verified by
Local File Search MCP (in-process)	`mcp/local_file_search.py` via FastMCP	`test_local_mcp.py`, E2E [1–3]
`search_files` metadata filters	name, folder, extension, dates, size	`test_search_files_*`
`search_pdf_content` full-text	pypdf keyword search	`test_search_pdf_content_keyword`
`list_all_files`	lists all sandboxed files	`test_list_all_files_returns_eight`, E2E [2]
`read_pdf_content`	read single PDF by path	`test_read_pdf_content_*`
Microsoft Learn MCP (remote)	`streamable_http` at learn.microsoft.com	`test_learn_mcp.py`, E2E [4]
SKILL routing (local JSON / MS prose / out-of-scope)	`SKILL.md`, `routing.py`, `output_guard.py`	`test_agent_routing.py`, E2E [5]
MiniMax via OpenAI-compatible API	`ChatOpenAI` + conditional `extra_body`	`agent_factory.py`, `test_agent_factory.py`, E2E all
Sandboxed `SEARCH_ROOT`	path traversal rejected	`test_security.py`
8 sample zoology files	`data/samples/zoology/`	`generate_samples.py`, E2E [2]

Dependencies

Pinned full environment (after pip install -e ".[dev]"):

pip install -r requirements.txt
pip install -e .

Or install from project metadata only: pip install -e ".[dev]".

GitHub

Published repository: https://github.com/dchatpar/mcp-file-agent

Reviewer abin-aot has been invited as a collaborator. Submission email draft for the AOT assessment: docs/SUBMISSION_EMAIL_TO_ABIN.md.

Project layout

src/file_search_agent/
  main.py              # Async REPL
  config.py            # Env config
  models.py            # Pydantic tool models
  agent_factory.py     # create_agent + MCP clients
  output_guard.py      # JSON / truncation guards
  mcp/local_file_search.py
data/samples/zoology/  # Non-tech zoology sample files
docs/                  # Full deployment and LLM guides
deploy/                # systemd unit example
Dockerfile             # Container image
docker-compose.yml
tests/

License

MIT

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