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isaac-levine/forage

by Various

Self-improving tool discovery for AI agents. Agents find, install, and learn to use new MCP tools automatically.

I

MCP

isaac-levine/forage

Added 1 June 2026

#ai-agent #claude #llm-tools #mcp #mcp-server #model-context-protocol #tool-discovery

Overview

A TypeScript project that enables AI agents to automatically discover, install, and learn to use new MCP tools. It aims to make tool discovery self-improving so agents can adapt their capabilities without manual intervention.

Best for

Best for
Developers building MCP-based AI agents that need dynamic tool discovery and integration

Use cases

  • Enabling an agent to find and add relevant MCP tools on the fly
  • Automating the installation of new tools during agent runtime
  • Teaching agents how to use discovered tools without prior configuration

How to use

Install

npx forage-mcp init --client cursor

Tools exposed

  • forage_search
  • forage_evaluate
  • forage_install
  • forage_learn
  • forage_status
  • forage_uninstall

Tested with

Claude Code, Cursor

Notes

A TypeScript project that enables AI agents to automatically discover, install, and learn to use new MCP tools. It aims to make tool discovery self-improving so agents can adapt their capabilities without manual intervention.

10 stars on GitHub. Last updated 2026-02-21. Licensed MIT.

Use cases

  • Enabling an agent to find and add relevant MCP tools on the fly
  • Automating the installation of new tools during agent runtime
  • Teaching agents how to use discovered tools without prior configuration

Pros

  • Reduces manual setup for tool integration in agent workflows
  • Self-improving mechanism allows agents to expand capabilities autonomously
  • Open source and focused on MCP ecosystem

Cons

  • Very small community (10 stars) suggests limited adoption and support
  • Dependent on MCP standard adoption and tool availability
  • Requires careful management of agent permissions for automatic installations

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Reduces manual setup for tool integration in agent workflows
  • Self-improving mechanism allows agents to expand capabilities autonomously
  • Open source and focused on MCP ecosystem

Cons

  • Very small community (10 stars) suggests limited adoption and support
  • Dependent on MCP standard adoption and tool availability
  • Requires careful management of agent permissions for automatic installations

Pairs with

Other entries in the index that connect to this one. Click through to see the chain.

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