evidence-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@evidence-mcpshow me the database schema"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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 syncRelated 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-mcpConfiguration
Environment variables:
Variable | Default | Description |
|
| Evidence dev server URL |
| - | Path to Evidence project |
|
| 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-mcpTo verify installation:
claude mcp listClaude 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-mcpThen 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 formatMaintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Servers
- FlicenseBqualityDmaintenanceEnables AI assistants to interact with GitHub repositories, Confluence documentation, and Databricks Unity Catalog through comprehensive tools for code exploration, documentation retrieval, and data schema management.Last updated19
- AlicenseAqualityCmaintenanceEnables AI assistants to interact with DBT (Data Build Tool) projects, allowing them to query project metadata, inspect models and sources, view compiled SQL, and run DBT commands.Last updated1414MIT
- Alicense-qualityAmaintenanceProvides MCP tools that help AI agents get their bearings in a codebase with unified SQL views over code, git, docs, and conversations, powered by DuckDB.Last updated4Apache 2.0
- Alicense-qualityCmaintenanceProvides AI assistants with 28 developer tools across file, git, code analysis, HTTP, and system domains, enabling tasks like file editing, repository management, code analysis, and shell command execution.Last updated332MIT
Related MCP Connectors
SEO research, audits, backlinks, GSC, and content workflow tools for AI agents.
Persistent memory and knowledge management for AI agents with semantic search and 50+ tools.
Connect your AI assistants to Keboola and expose your data, transformations, SQL queries, ...
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/jaho5/evidence-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server