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Eltortilla1/synapse-code-mcp

by Various

Model Context Protocol server for code intelligence. Lets AI assistants explore your codebase on demand — semantic context with dependency graphs, compressed symbol indexes for Typ

E

MCP

Eltortilla1/synapse-code-mcp

Added 26 June 2026

Overview

Eltortilla1/synapse-code-mcp is a Model Context Protocol server for code intelligence. It lets AI assistants explore codebases on demand by providing semantic context with dependency graphs and compressed symbol indexes for TypeScript and JavaScript. It also offers git diffs and ripgrep-powered search across any language.

Best for

Best for
Developers building AI-assisted code exploration tools for TypeScript and JavaScript projects

Use cases

  • Retrieve dependency graphs for TypeScript modules via an AI assistant
  • Search codebase with ripgrep using natural language queries
  • Get git diff context for code review or debugging

Notes

Eltortilla1/synapse-code-mcp is a Model Context Protocol server for code intelligence. It lets AI assistants explore codebases on demand by providing semantic context with dependency graphs and compressed symbol indexes for TypeScript and JavaScript. It also offers git diffs and ripgrep-powered search across any language.

1 stars on GitHub. Last updated 2026-06-21. Licensed MIT.

Use cases

  • Retrieve dependency graphs for TypeScript modules via an AI assistant
  • Search codebase with ripgrep using natural language queries
  • Get git diff context for code review or debugging

Pros

  • Provides rich code intelligence including dependency graphs and symbol indexes
  • Works across any language for search via ripgrep
  • Integrates with AI assistants through the standard MCP protocol

Cons

  • Compressed symbol indexes are limited to TypeScript and JavaScript
  • Low community adoption and support (1 star on GitHub)
  • Requires an MCP-compatible AI assistant to function

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

Pros

  • Provides rich code intelligence including dependency graphs and symbol indexes
  • Works across any language for search via ripgrep
  • Integrates with AI assistants through the standard MCP protocol

Cons

  • Compressed symbol indexes are limited to TypeScript and JavaScript
  • Low community adoption and support (1 star on GitHub)
  • Requires an MCP-compatible AI assistant to function