Fake Pennylane MCP Server

Fake Pennylane MCP Server

A fake MCP server that emulates the Pennylane MCP experience for local development, testing, demos, and CI pipelines, providing deterministic fixtures and authentication modes without needing a real Pennylane account.

Category
Visit Server

README

Fake Pennylane MCP Server

An open-source fake server that emulates the Pennylane MCP experience for local development, automated tests, demos, and CI pipelines — without requiring a real Pennylane account or real accounting data.

Status: working early implementation. The repository now includes a runnable Streamable HTTP MCP server, deterministic fixtures, scenario packs, static bearer auth, and a fake OAuth flow for callback/integration testing.

Why this project exists

Pennylane MCP is useful, but it is hard to validate an integration when you do not have:

  • a real Pennylane account,
  • a portfolio of test companies and customers,
  • deterministic accounting fixtures,
  • a safe environment for demos and CI.

This project solves that gap by providing a predictable, developer-friendly test double for a Pennylane-like MCP server.

Current capabilities

The current implementation provides:

  • a Streamable HTTP MCP server,
  • deterministic fixture loading from JSON,
  • scenario-pack selection,
  • optional static bearer token authentication,
  • a fake OAuth authorization-code flow for end-to-end callback testing,
  • automated local tests for runtime, scenarios, and auth.

Exposed tools

  • get_context
  • get_company
  • list_companies
  • list_customers
  • get_customer
  • list_customer_invoices

Quickstart

Requirements

  • Python 3.11+
  • uv

Install dependencies

cd /home/leyriel/dev/fake-pennylane-mcp-server
uv sync

Run the server

Default fixture dataset, no auth:

uv run python -m fake_pennylane_mcp_server --host 127.0.0.1 --port 8000

With a scenario pack:

uv run python -m fake_pennylane_mcp_server \
  --host 127.0.0.1 \
  --port 8000 \
  --scenario unpaid_invoice

With static bearer auth:

uv run python -m fake_pennylane_mcp_server \
  --host 127.0.0.1 \
  --port 8000 \
  --auth-mode static_bearer \
  --bearer-token test-secret-token

With fake OAuth flow:

uv run python -m fake_pennylane_mcp_server \
  --host 127.0.0.1 \
  --port 8000 \
  --auth-mode fake_oauth

Run with Docker Compose

Build and start the default server:

docker compose up --build

Run detached:

docker compose up --build -d

Stop it:

docker compose down

If port 8000 is already used on your machine, override the published host port:

PUBLISHED_PORT=8080 docker compose up --build -d

Use a scenario pack:

SCENARIO_NAME=unpaid_invoice docker compose up --build

Use static bearer auth:

AUTH_MODE=static_bearer BEARER_TOKEN=test-secret-token docker compose up --build

Use fake OAuth mode:

AUTH_MODE=fake_oauth docker compose up --build

By default Compose exposes the server on http://127.0.0.1:${PUBLISHED_PORT:-8000}/mcp and mounts ./fixtures read-only into the container.

Scenario packs

The default fixture file currently exposes these scenario packs:

  • default
  • customer_found
  • customer_not_found
  • unpaid_invoice
  • multi_company

Each scenario narrows the deterministic dataset to a stable business situation.

Authentication modes

none

Default mode. No Authorization header is required.

static_bearer

Requires a bearer token on MCP HTTP requests.

Example header:

Authorization: Bearer ***

fake_oauth

Exposes a minimal fake OAuth authorization server alongside the MCP resource server.

Included endpoints:

  • /.well-known/oauth-authorization-server
  • /.well-known/oauth-protected-resource/mcp
  • /authorize
  • /token

Current fake client contract:

  • client_id: fake-public-client
  • token endpoint auth method: none
  • redirect URIs:
    • http://127.0.0.1:9999/callback
    • http://localhost:9999/callback
  • scope: mcp:access

This flow is meant for local integration tests and fake callback simulations — not as a production OAuth server.

Compatibility and status tracking

See:

  • docs/compatibility-matrix.md
  • docs/README.md

Example clients

See:

  • examples/basic_client.py
  • examples/bearer_client.py
  • examples/README.md

Running tests

uv run pytest -q

Development workflow

Typical local loop:

  1. update fixtures / scenarios / auth behavior,
  2. run uv run pytest -q,
  3. run one of the example clients against a local server,
  4. commit the change.

Project structure

.
├── docs/
├── examples/
├── fixtures/
├── src/
├── tests/
├── CONTRIBUTING.md
├── Dockerfile
├── docker-compose.yml
├── LICENSE
├── README.md
└── pyproject.toml

Roadmap

Bootstrap

  • [x] Create Plane project
  • [x] Create public GitHub repository
  • [x] Publish initial detailed README
  • [x] Prepare repo structure for implementation handoff

Implementation milestones

  • [x] Implement fake Streamable HTTP MCP runtime
  • [x] Add deterministic fixture loading
  • [x] Add fake customer / invoice queries
  • [x] Add scenario packs
  • [x] Add auth modes for integration testing
  • [x] Add runnable example clients
  • [x] Add fake OAuth flow for end-to-end callback testing
  • [x] Publish a compatibility matrix
  • [x] Add Docker and Docker Compose local runtime

Next useful milestones

  • [ ] add GitHub Actions CI once a token with workflow scope is available
  • [ ] enrich compatibility with more Pennylane-like tools
  • [ ] add more domain fixture packs
  • [ ] add standalone OAuth example client
  • [ ] document additional integration recipes

Open-source direction

The intent is for this project to be genuinely useful to a broader audience, not just a one-off internal spike.

That means we optimize for:

  • clarity of documentation,
  • ease of local setup,
  • stable deterministic fixtures,
  • transparent scope,
  • simple contribution paths.

Project origin

This repository was spun out from work around a MissionGuard integration effort, where a Pennylane MCP replacement strategy needed a realistic test environment without depending on a real Pennylane tenant.

License

MIT.

Disclaimer

This project is not affiliated with or endorsed by Pennylane. It is an independent open-source testing utility intended to emulate part of a Pennylane-like MCP integration surface for development and testing purposes only.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured