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langchain-docs-local-mcp

by xmkoh
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Python
Hybrid

langchain-docs-local-mcp

An offline MCP server that provides LangChain and LangGraph documentation search — a drop-in replacement for the cloud-hosted mcp__langchain-docs__ server for airgapped environments.

Why

In airgapped environments the cloud MCP server for LangChain docs is unreachable. This project bundles the documentation locally and serves it via a local MCP server using BM25 full-text search — no network access, no ML model downloads, no Docker required at runtime.

Related MCP server: MCP Web Docs

Tools exposed

Tool

Description

search_docs_by_lang_chain

BM25 search across bundled LangChain/LangGraph docs

query_docs_filesystem_docs_by_lang_chain

Shell-like access to doc files (cat, head, ls, find, rg)

These names match the original cloud MCP server exactly for drop-in compatibility.

Local quick start (stdio)

# 1. Clone and install
git clone https://github.com/xmkoh/langchain-docs-local-mcp
cd langchain-docs-local-mcp
pip install -r requirements.txt

# 2. Build the search index from the bundled docs
python scripts/build_index.py

# 3. Test
pip install pytest
pytest tests/

# 4. Wire up Claude Code — edit the path, then restart Claude Code
cp .claude/settings.json /path/to/your/project/.claude/settings.json

.claude/settings.json (stdio):

{
  "mcpServers": {
    "langchain-docs": {
      "type": "stdio",
      "command": "python",
      "args": ["server.py"],
      "cwd": "/absolute/path/to/langchain-docs-local-mcp"
    }
  }
}

The tools appear in Claude Code as:

  • mcp__langchain-docs__search_docs_by_lang_chain

  • mcp__langchain-docs__query_docs_filesystem_docs_by_lang_chain

Kubernetes / OpenShift deployment

1. Build and push the image

The image bakes the docs and pre-built index in at build time — no volumes or init containers needed.

docker build -t registry.example.com/langchain-docs-local-mcp:0.1.0 .
docker push registry.example.com/langchain-docs-local-mcp:0.1.0

The image defaults to SSE transport on port 8000. Switch to streamable-http via the MCP_TRANSPORT env var or --transport flag.

2. Deploy with Helm

# OpenShift — with a public Route (TLS edge termination)
helm install langchain-docs ./helm/langchain-docs-mcp \
  --namespace my-namespace \
  --set image.repository=registry.example.com/langchain-docs-local-mcp \
  --set image.tag=0.1.0 \
  --set route.enabled=true

# Plain Kubernetes — with an Ingress instead
helm install langchain-docs ./helm/langchain-docs-mcp \
  --namespace my-namespace \
  --set image.repository=registry.example.com/langchain-docs-local-mcp \
  --set image.tag=0.1.0 \
  --set ingress.enabled=true \
  --set ingress.hosts[0].host=langchain-docs-mcp.example.com \
  --set ingress.hosts[0].paths[0].path=/ \
  --set ingress.hosts[0].paths[0].pathType=Prefix

After install, Helm prints the Claude Code config snippet automatically (via NOTES.txt).

3. Get the Route URL (OpenShift)

oc get route langchain-docs-langchain-docs-mcp -n my-namespace \
  -o jsonpath='{.spec.host}'

4. Wire up Claude Code (network transport)

SSE (default, mcpTransport: sse):

{
  "mcpServers": {
    "langchain-docs": {
      "type": "sse",
      "url": "https://<ROUTE_HOST>/sse"
    }
  }
}

Streamable HTTP (--set mcpTransport=streamable-http):

{
  "mcpServers": {
    "langchain-docs": {
      "type": "http",
      "url": "https://<ROUTE_HOST>/mcp"
    }
  }
}

Port-forward (no Route/Ingress, in-cluster access only):

kubectl port-forward svc/langchain-docs-langchain-docs-mcp 8000:8000 -n my-namespace
{
  "mcpServers": {
    "langchain-docs": {
      "type": "sse",
      "url": "http://localhost:8000/sse"
    }
  }
}

OpenShift security notes

The image is built to work with the default restricted SCC — no anyuid or elevated permissions required:

  • Files are owned 1001:0 with chmod g=u so OpenShift's arbitrary UID (always GID 0) can read them

  • runAsNonRoot: true, allowPrivilegeEscalation: false, all capabilities dropped

  • readOnlyRootFilesystem: true with an /tmp emptyDir for Python's tempfile

Key Helm values

Value

Default

Description

image.repository

langchain-docs-local-mcp

Image to deploy

image.tag

chart appVersion

Image tag

mcpTransport

sse

sse or streamable-http

route.enabled

false

Create an OpenShift Route

route.tls.enabled

true

TLS on the Route

route.tls.termination

edge

edge, passthrough, or reencrypt

ingress.enabled

false

Create a Kubernetes Ingress

autoscaling.enabled

false

Enable HorizontalPodAutoscaler

replicaCount

1

Number of replicas

Updating the documentation

Documentation is bundled at image build time. To update after an upstream LangChain/LangGraph release:

# On a machine with internet access:
pip install httpx
python scripts/fetch_docs.py       # re-downloads from GitHub
python scripts/build_index.py      # rebuilds index/bm25_index.pkl

git add docs/ index/
git commit -m "Update docs"
git push

# Then rebuild and push a new image:
docker build -t registry.example.com/langchain-docs-local-mcp:0.2.0 .
docker push registry.example.com/langchain-docs-local-mcp:0.2.0
helm upgrade langchain-docs ./helm/langchain-docs-mcp --set image.tag=0.2.0

fetch_docs.py prints a warning for any URL that fails — this happens when LangChain reorganizes their docs. Update the FETCH_TARGETS list in scripts/fetch_docs.py with the new GitHub raw URL.

Adding your own documentation

Drop any .md file anywhere under docs/ and rebuild the index and image:

python scripts/build_index.py
docker build -t registry.example.com/langchain-docs-local-mcp:custom .

Offline pip installs (fully airgapped)

# On a networked machine — bundle wheels alongside the repo:
pip download -r requirements.txt -d wheels/
git add wheels/ && git commit -m "Add offline wheels"

# On the airgapped machine:
pip install --no-index --find-links wheels/ -r requirements.txt

Documentation included

The repository ships with hand-curated documentation for:

  • LangGraph: BaseStore, InMemoryStore, MemorySaver, StateGraph, checkpointers, persistence, human-in-the-loop

  • Deep Agents: architecture overview, memory patterns (short-term vs long-term), namespace conventions

  • LangChain: @tool, StructuredTool, BaseTool, create_react_agent, AgentExecutor, streaming

See docs/README.md for full coverage.

Architecture

server.py              FastMCP server — stdio, SSE, or streamable-http transport
search.py              BM25 search engine (rank_bm25), loaded once at startup
Dockerfile             Builds a self-contained image with docs + index baked in
helm/
  langchain-docs-mcp/  Helm chart for Kubernetes / OpenShift
    templates/
      deployment.yaml  Pod with restricted SCC security context
      service.yaml     ClusterIP on port 8000
      route.yaml       OpenShift Route (route.enabled=true)
      ingress.yaml     Kubernetes Ingress (ingress.enabled=true)
      hpa.yaml         HorizontalPodAutoscaler (autoscaling.enabled=true)
docs/                  Bundled markdown documentation
index/
  bm25_index.pkl       Pre-built search index (committed to git)
scripts/
  build_index.py       Rebuild the index from docs/
  fetch_docs.py        Download docs from GitHub (run once, online)

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