LKPlanWise MCP Server
Stateless MCP adapter for the LKPlanWise Go REST API, enabling AI agents to call curated financial tools via Streamable HTTP while forwarding bearer credentials.
README
LKPlanWise MCP Server
Stateless MCP adapter for the LKPlanWise Go REST API. AI clients call curated MCP tools over Streamable HTTP; this service validates tool contracts and forwards each caller's Bearer credential to the Go backend. It does not connect to PostgreSQL or duplicate financial business rules.
Architecture
AI Agent
-> MCP Streamable HTTP
-> Python FastMCP server
-> HTTPX + request-scoped Bearer credential
-> LKPlanWise Go API
-> service layer
-> PostgreSQL
Requirements
- Python 3.12+
- uv
- Running LKPlanWise Go backend
Setup
uv python install 3.12
uv sync
Copy-Item .env.example .env
Configure LKPLANWISE_MCP_BACKEND_BASE_URL in .env, then run:
uv run lkplanwise-mcp-server
The MCP endpoint is available at:
http://127.0.0.1:8000/mcp
The operational health endpoint is:
http://127.0.0.1:8000/healthz
First tool
list_financial_accounts lists accounts owned by the authenticated user. The
MCP request must include:
Authorization: Bearer <credential>
The credential is forwarded to GET /financial-accounts on the Go backend.
The Go backend remains responsible for validating the credential, resolving
the user, enforcing ownership, and applying business rules.
The current Go middleware accepts JWT access tokens. Before using sk-lk-*
Agent API keys end to end, add a restricted Agent-authenticated route group to
the Go backend or extend only the intended resource routes with Agent API key
authentication.
Development
uv run pytest
uv run ruff check .
uv run ruff format --check .
uv run mypy
Test the running server with MCP Inspector:
npx -y @modelcontextprotocol/inspector
Connect the Inspector to http://127.0.0.1:8000/mcp and provide the
Authorization header in its connection settings.
Design rules
- Tools are curated user actions, not an automatic mirror of REST endpoints.
- The MCP service never connects directly to the database.
- Credentials are request-scoped and must never be logged or returned.
- Reads and writes are separate tools with accurate safety annotations.
- Consequential write tools require idempotency support in the Go backend.
Recommended Servers
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.
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.
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.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
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.
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.
Neon Database
MCP server for interacting with Neon Management API and databases
E2B
Using MCP to run code via e2b.
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.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.