Unified MCP server with hybrid search, code graph analysis, and semantic memory
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# Nexus-MCP [](https://pypi.org/project/nexus-mcp-ci/) [](https://pypi.org/project/nexus-mcp-ci/) [](LICENSE) [](tests/) [](https://glama.ai/mcp/servers/jaggernaut007/Nexus-MCP) **Hybrid search + code graph + semantic memory in a single local MCP server — under 350 MB RAM.** 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-ci -- nexus-mcp-ci ``` --- ## The Problem It Solves AI coding agents are token-inefficient by default. An agent trying to understand `verify_credentials()` typically: 1. `Glob("src/**/*.py")` → 120 files returned, agent reads the most likely 8 → **~12,000 tokens** 2. `Grep("verify_credentials")` → 3 matches, agent reads surrounding context → **~4,000 tokens** 3. `Read("auth/middleware.py")` → full 400-line file to understand callers → **~3,000 tokens** **Total: ~19,000 tokens, 3+ tool calls, no graph relationships.** With Nexus-MCP: 1. `explain("verify_credentials")` → symbol definition + all callers + all callees + complexity metrics → **~1,500 tokens, 1 tool call** Or for discovery: 1. `search("credential verification flow")` → top-10 semantically relevant chunks across the codebase → **~2,000 tokens, 1 tool call** **Estimated savings: 30–60% token reduction per coding session.** The exact numbers depend on codebase size and task type — see the [benchmarks table](#token-efficiency) below. --- ## Quickstart (60 seconds) ```bash…
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