context-link v1.0.0: Local MCP server reduces Claude Code token usage by 91%
What context-link does
context-link v1.0.0 is a local Model Context Protocol (MCP) server that addresses Claude Code's inefficient file reading behavior. Instead of reading entire files to understand a single function, it indexes your codebase with Tree-sitter and serves Claude only the exact symbols, dependencies and structure it needs.
Example from the source: Instead of reading auth.go (3,200 tokens) to find validateToken(), Claude gets just that function + its dependencies (288 tokens).
Key features in v1.0.0
- 11 supported languages: Go, Python, TypeScript, Rust, Java, C/C++, C#, JavaScript, etc. (You can add more using the Language Registry)
- Hybrid search: vector embeddings + keyword matching
- Git-aware context: shows which symbols you've modified in your working tree
- Regex pattern search across symbol bodies
- Call trees, blast radius, dead code, test linking
- Batch operations — 50 file skeletons or symbols in one call
- Every response shows tokens_saved_est and cost_avoided_est
- explore_codebase: Built-in prompt that teaches the agent the optimized workflow
Performance benchmarks
- 91% token reduction in specific cases (3,200 tokens → 288 tokens)
- 70-80% fewer tokens across full tasks when audited by another Claude Code instance on multiple Python and Go codebases
- Semantic search in 197µs
- Incremental re-index in <10ms
- Runs fully local, no cloud
Technical details
The tool is licensed under Apache-2.0 and available at https://github.com/context-link-mcp/context-link. According to the source, it "stacks well with Headroom + RTK if you're layering token savings."
📖 Read the full source: r/ClaudeAI
👀 See Also
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ClawCut Proxy Released on GitHub to Optimize OpenClaw for Small LLMs
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Claude Code Session Dashboard: Open Source Tool for Monitoring Multiple Sessions
An open-source dashboard that monitors multiple Claude Code sessions simultaneously, showing token usage, costs, session status, context window usage, and active subagents. Installation requires three commands: git clone, cd, and npm install && npm start.
Karpathy's autoresearch project: AI agents run overnight LLM training experiments
Andrej Karpathy released a minimal autoresearch project where an AI agent edits train.py, runs 5-minute nanochat training experiments, checks if val_bpb improved, and repeats overnight on a single GPU.