Local MCP server for AI coding agents: hybrid code search, static call graph and project memory in fewer agent turns
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# Nexus-MCP [](https://pypi.org/project/nexus-mcp-ci/) [](https://pypistats.org/packages/nexus-mcp-ci) [](https://pypi.org/project/nexus-mcp-ci/) [](#license) [](https://github.com/jaggernaut007/Nexus-MCP/actions/workflows/publish.yml) [](https://glama.ai/mcp/servers/jaggernaut007/Nexus-MCP) **Hybrid search + code graph + semantic memory in a single local MCP server. About 90 MB idle and 460 MB with the embedding model loaded ([measurements](docs/MEMORY.md)).** Nexus-MCP is a code intelligence server for the [Model Context Protocol](https://modelcontextprotocol.io). It gives AI agents precise, token-efficient answers about your codebase without cloud dependencies: no API keys, no data egress, no subscriptions. ``` pip install nexus-mcp-ci claude mcp add nexus-mcp -- nexus-mcp-ci ``` --- ## The Problem It Solves An agent that explores a codebase with Grep and Read pays for every call: each call is one more turn, and each turn reads the whole conversation again. Nexus-MCP answers a question about code in fewer calls. `search` returns the whole code of its top results, `find_symbol` returns the code of several names in one call, and `graph` returns every text reference beside the call edges. The north star of the project is lower token use with the same accuracy. It is measured, not estimated: see [Token efficiency](#token-efficiency) and `benchmarks/`. --- ## Quickstart (60 seconds) ```bash # 1. Install pip ins…
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