octocode-mcp and snippy
These are complementary tools: octocode-mcp provides semantic code search across repositories via natural language queries, while snippy provides the infrastructure (Azure Functions, vector search, multi-agent orchestration) to build similar AI-powered code indexing and retrieval systems.
About octocode-mcp
bgauryy/octocode-mcp
MCP server for semantic code research and context generation on real-time using LLM patterns | Search naturally across public & private repos based on your permissions | Transform any accessible codebase/s into AI-optimized knowledge on simple and complex flows | Find real implementations and live docs from anywhere
Implements MCP (Model Context Protocol) with LSP-powered code intelligence (Go to Definition, Find References, Call Hierarchy) across GitHub, GitLab, and local codebases, enabling compiler-level understanding without parsing. Provides modular Agent Skills—including multi-phase research with session persistence, AST-driven code analysis, dependency graphing, and PR review across seven domains—composable via CLI or direct integration into Claude/Cursor.
About snippy
Azure-Samples/snippy
🧩 Build AI-powered MCP Tools with Azure Functions, Durable Agents & Cosmos vector search. Features orchestrated multi-agent workflows using OpenAI.
Exposes Azure Functions as discoverable MCP tools for AI assistants like GitHub Copilot, while using Durable Task Scheduler to orchestrate multi-agent workflows (fan-out/fan-in patterns) and Cosmos DB with DiskANN vector indexing for semantic code search. Includes reproducible infrastructure-as-code via `azd` that deploys the entire stack locally with Docker emulators or to Azure cloud, with real-time orchestration monitoring through DTS dashboard.
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