outreach-mcp-server
MCP server that provides tools for cold email outreach, including checking contact history, drafting personalized emails, and retrieving performance summaries from past outreach data.
README
outreach-mcp-server
An MCP (Model Context Protocol) server that exposes the core capabilities of outreach-agent — a Python cold outreach agent using the Anthropic API and Gmail API — as standard MCP tools.
Instead of a one-off script that only I can run, this lets any MCP client (Claude Desktop, Claude.ai, or any other MCP-compatible agent host) connect and call the same drafting and dedup logic directly, with no custom integration code per client.
Tools
check_contact_history(name)— checks whether a person is already queued, drafted, or missing an email in the outreach pipeline, to prevent duplicate outreach.draft_outreach_email(name, role, institution, research_area, email, notes)— drafts a personalized cold outreach email using the agent's tone rules and prior send/reply performance data as context. Returns the draft only; does not send anything.get_outreach_performance_summary()— returns a summary of what has and hasn't worked, learned from past email outcomes.
Why MCP instead of just tool-calling
The underlying outreach-agent project already does tool-calling: Claude decides to draft an email, my code executes it. MCP is a layer above that — it turns those same capabilities into a discoverable server that any MCP client can plug into, without me writing custom glue code for each one. This repo is that server.
Setup
pip install mcp anthropic python-dotenv
export ANTHROPIC_API_KEY=sk-...
python3 server.py
demo_targets.csv, demo_drafted.csv, and demo_memory.md are synthetic
sample data (not real contacts) included so the server is runnable and
testable out of the box. Point TARGETS_CSV / DRAFTED_CSV / MEMORY_MD in
server.py at real pipeline files to use it against the live agent's data.
Testing
test_client.py spins up a real MCP client over stdio, lists the registered
tools, and calls each one — a quick way to verify the server works end to
end without needing a full MCP host like Claude Desktop.
python3 test_client.py
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.
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.
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.
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.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
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.