Pandas Excel Analytics MCP

A deterministic Model Context Protocol (MCP) server exposing Pandas, NumPy, Excel/openpyxl, and automated EDA operations over uploaded tabular datasets — built to be driven by an LLM agent (e.g. Langflow + OpenRouter) through the MCP tool-calling protocol.

This server contains no LLM, no embeddings, no RAG, and no Langflow/OpenRouter credentials. It is purely the deterministic data-execution layer: 27 tools across dataset management, Pandas, NumPy, Excel automation, EDA, and export.

Built on the current stable MCP Python SDK (mcp v2.x), where the server class is mcp.server.mcpserver.MCPServer — the successor to the pre-2.0 mcp.server.fastmcp.FastMCP name used in older tutorials. The public API (.tool(), .streamable_http_app()) is unchanged, just the import path.

Architecture

User
  |
Langflow (orchestration + agent reasoning)
  |
OpenRouter LLM (base LLM agent)
  |
MCP Client (Tool Mode / Toolset)
  |  HTTPS · Streamable HTTP · Bearer auth
  v
Render — Pandas Excel Analytics MCP  (/mcp, /health)
  |
Dataset Manager (dataset_id, session, validation)
  |
  +---------------+---------------+---------------+
  |               |               |               |
Pandas Engine  NumPy Engine   Excel Engine   Cleaning Engine
  |               |               |               |
  +---------------+---------------+---------------+
                  |
              EDA Engine (profiling, data-quality score)
                  |
        +---------+---------+
        |         |         |
       CSV      XLSX      JSON

The MCP server never calls an LLM, never holds OPENROUTER_API_KEY, and never executes arbitrary code — it only receives validated tool calls and runs deterministic Pandas/NumPy/openpyxl operations.

Related MCP server: CSV MCP Assistant

The 27 tools

Local setup

git clone https://github.com/varunmulay-droid/Pandas-excel-numpy-EDA-MCP.git
cd Pandas-excel-numpy-EDA-MCP
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

Run the server:

uvicorn server:app --reload --host 127.0.0.1 --port 8000

• MCP endpoint: http://127.0.0.1:8000/mcp

• Health check: http://127.0.0.1:8000/health → {"status":"ok"}

Inspect and call tools manually:

npx @modelcontextprotocol/inspector

Connect using transport "Streamable HTTP" to http://127.0.0.1:8000/mcp.

Run the test suite (43 tests):

pytest tests/ -v

Deploying to Render — step by step

1. Push your code to GitHub

Already done if you're reading this from the repo. Otherwise:

git add -A && git commit -m "deploy" && git push

2. Create the service on Render

1. Go to render.com → New + → Blueprint.

2. Connect the Pandas-excel-numpy-EDA-MCP GitHub repo.

3. Render detects render.yaml in the repo root and pre-fills:

   • Build command: pip install -r requirements.txt

   • Start command: uvicorn server:app --host 0.0.0.0 --port $PORT

   • Health check path: /health

4. Click Apply to create the service.

3. Set environment variables

Render auto-generates MCP_API_TOKEN (via generateValue: true in render.yaml). After the first deploy:

1. Open the service → Environment tab.

2. Copy the generated MCP_API_TOKEN value — you'll need it for your MCP client's Authorization: Bearer <token> header.

3. Update MCP_ALLOWED_HOSTS to match the exact hostname Render assigned to your service, e.g. pandas-excel-numpy-eda-mcp.onrender.com (Render shows this at the top of the service dashboard once created). This step is required — the MCP SDK rejects any request whose Host header isn't in this allowlist, by design.

4. Deploy

Render builds and deploys automatically on every push to main. Watch the Logs tab for:

INFO:     Uvicorn running on http://0.0.0.0:10000

5. Verify the deployment

curl https://YOUR-SERVICE.onrender.com/health
# {"status":"ok"}

Then verify the MCP endpoint itself with MCP Inspector, pointing at:

https://YOUR-SERVICE.onrender.com/mcp

and adding header Authorization: Bearer <MCP_API_TOKEN> if auth is enabled.

Production MCP URL format:

https://YOUR-SERVICE.onrender.com/mcp

Environment variables

OPENROUTER_API_KEY and any LLM credentials are intentionally not consumed by this server — keep those in Langflow, not here.

How to connect it further

Connect to Langflow (next step)

1. In Langflow, add an MCP Tools (MCP Client) component.

2. Set the server URL to https://YOUR-SERVICE.onrender.com/mcp, transport Streamable HTTP, and header Authorization: Bearer <MCP_API_TOKEN>.

3. Set the component to Tool Mode — all 27 tools populate as a Toolset.

4. Wire that Toolset into your Base LLM Agent node (OpenRouter).

5. Test with a prompt like: "Upload sales.csv and tell me which region has the highest profit."

Connect an OpenRouter-backed agent

Keep OPENROUTER_API_KEY in Langflow's LLM node, not in this server — the MCP server should stay a pure tool provider so it can be reused by any agent framework (Langflow, LangChain, Claude, custom MCP clients) without carrying LLM-specific config.

Future: embeddings / RAG (V3+)

Not needed for direct data operations ("group sales by region"). Consider adding a vector store only when you need semantic search over dataset metadata — e.g. "what does this column mean?" — layered in front of this MCP server, not inside it.

Future: persistence (V2+)

Datasets currently live in server memory and are lost on restart/redeploy. For persistent, multi-instance deployments, swap DatasetManager's in-memory dict for object storage (S3-compatible) + a metadata database, without changing any tool signatures.

Security notes

• No eval/exec/shell execution/arbitrary imports anywhere in the codebase.

• Every dataset is addressed by an opaque dataset_id — callers never supply filesystem paths.

• Filenames are sanitised and path-joined under a fixed storage root (utils/security.py::safe_join) — path traversal is structurally impossible.

• TransportSecuritySettings host allowlist is always configured; the SDK's default localhost-only protection is never disabled.

• Bearer-token auth is opt-in via MCP_API_TOKEN; when unset the server runs unauthenticated (fine for local dev, not for a public Render URL).

Project layout

pandas-excel-analytics-mcp/
├── server.py                  # MCP app entrypoint (Streamable HTTP, /mcp, /health)
├── requirements.txt
├── pyproject.toml
├── render.yaml                 # Render Blueprint
├── architecture.svg
├── src/pandas_excel_mcp/
│   ├── config/settings.py      # env-driven limits & config
│   ├── core/                   # dataset/session/file managers, validators
│   ├── engines/                # actual Pandas/NumPy/Excel/EDA logic
│   ├── tools/                  # thin MCP tool wrappers (27 tools)
│   ├── schemas/                # exceptions + response envelopes
│   └── utils/                  # serialization, security, logging
└── tests/                      # 43 pytest tests