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jaho5

evidence-mcp

by jaho5
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Evidence MCP Server

An MCP (Model Context Protocol) server that provides tools for AI assistants to help users create Evidence reports and dashboards.

Installation

# Clone and install
git clone https://github.com/jaho5/evidence-mcp.git
cd evidence-mcp
uv sync

Related MCP server: DBT Core MCP Server

Usage

# Run the MCP server
uv run evidence-mcp

# With custom Evidence project path
EVIDENCE_MCP_EVIDENCE_PROJECT_PATH=/path/to/project uv run evidence-mcp

Configuration

Environment variables:

Variable

Default

Description

EVIDENCE_MCP_EVIDENCE_DEV_URL

http://localhost:3000

Evidence dev server URL

EVIDENCE_MCP_EVIDENCE_PROJECT_PATH

-

Path to Evidence project

EVIDENCE_MCP_TRANSPORT

stdio

Transport mode: stdio, sse

Tools

get_metadata

Returns database schema from Evidence's DuckDB connection.

read_docs

Retrieves Evidence documentation using hierarchical lookup.

edit_page

Proposes changes to the current Evidence markdown page.

debug_code

Analyzes validation errors and suggests fixes.

Claude Code Setup

Add to your Claude Code MCP settings (~/.claude.json):

{
  "mcpServers": {
    "evidence-mcp": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/evidence-mcp", "evidence-mcp"],
      "env": {
        "EVIDENCE_MCP_EVIDENCE_PROJECT_PATH": "/path/to/your/evidence/project"
      }
    }
  }
}

Or add via CLI:

claude mcp add evidence-mcp -- uv run --directory /path/to/evidence-mcp evidence-mcp

To verify installation:

claude mcp list

Claude Desktop Setup

Add to Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS or %APPDATA%\Claude\claude_desktop_config.json on Windows):

{
  "mcpServers": {
    "evidence-mcp": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/evidence-mcp", "evidence-mcp"],
      "env": {
        "EVIDENCE_MCP_EVIDENCE_PROJECT_PATH": "/path/to/your/evidence/project"
      }
    }
  }
}

Programmatic Usage (MCP Client)

import asyncio
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

async def main():
    server_params = StdioServerParameters(
        command="uv",
        args=["run", "--directory", "/path/to/evidence-mcp", "evidence-mcp"],
        env={
            "EVIDENCE_MCP_EVIDENCE_PROJECT_PATH": "/path/to/your/evidence/project"
        }
    )

    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            # Initialize the connection
            await session.initialize()

            # List available tools
            tools = await session.list_tools()
            print("Available tools:", [t.name for t in tools.tools])

            # Call get_metadata
            result = await session.call_tool("get_metadata", arguments={})
            print("Metadata:", result.content)

            # Call read_docs
            result = await session.call_tool("read_docs", arguments={
                "doc_type": "charts",
                "component": "LineChart"
            })
            print("Docs:", result.content)

asyncio.run(main())

With OpenAI Agents SDK

First, run the server in SSE mode:

EVIDENCE_MCP_TRANSPORT=sse \
EVIDENCE_MCP_EVIDENCE_PROJECT_PATH=/path/to/your/evidence/project \
uv run evidence-mcp

Then use HostedMCPTool to connect:

from agents import Agent, HostedMCPTool

agent = Agent(
    name="Evidence Assistant",
    instructions="Help users create Evidence reports and dashboards.",
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "evidence",
                "server_url": "http://localhost:8000/sse",
                "require_approval": "never",
            }
        )
    ],
)

Development

# Install with dev dependencies
uv sync --extra dev

# Run tests
uv run pytest

# Run tests with coverage
uv run pytest --cov=evidence_mcp

# Lint
uv run ruff check

# Format
uv run ruff format
Install Server
F
license - not found
B
quality
D
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

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