Pre-curated canonical memory + prose/code provenance for any AI coding client. MCP-native.
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# Librarian MCP **A real, measured alternative to "bigger context windows."** Pre-curated canonical memory + prose/code provenance checking + benchmark metrics, delivered as a [Model Context Protocol](https://modelcontextprotocol.io) server that works across Claude Code, Cursor, VSCode (via Continue), and any MCP-capable client. ## What it does Five tools, all exposed via MCP: | Tool | What it does | Added | |---|---|---| | **`librarian_context`** | Intent-aware canonical memory packet. Loads curated preload content scoped to your query intent (outreach, architecture, benchmark, founder voice, etc.). Eliminates the "forgets by prompt #21" failure mode. | v0.1.0 (stub), **v0.2.0** (intent-aware) | | **`prose_provenance`** | Deterministic drift detection between two document versions. Catches silently-removed voice anchors, stale canonical numbers, section changes, register shifts. | v0.1.0 | | **`record_measurement`** | Log a single benchmark measurement (vendor, model, condition, accuracy, cost, latency) to local JSONL. | **v0.2.0** | | **`metrics_summary`** | Per-vendor and per-model aggregation of recorded measurements. Shows accuracy lift, cost savings, cache hit rate. | **v0.2.0** | | **`opt_in_share`** | Toggle anonymous metrics sharing flag. Default OFF. Commons dashboard POST endpoint ships in a future release. | **v0.2.0** | ## Why we built this Independently measured result (Eyewitness Benchmark R10, April 2026, eight models across four vendors, 1,200 graded calls, inter-rater kappa 0.883/0.850): - **Without the Librarian (COLD):** mean 8.7% correct - **With the Librarian (HOT):** mean 94.8% correct — **86.1 percentage-point lift** - **Haiku 4.5 (cheapest) ties Opus 4.7 (most expensive)** at 19x cost difference - **4.3x more right answers per dollar of compute** Applied inside Microsoft Copilot's inference path, the same architecture recovers an estimated **$750M/year** in waste. Inside Anthropic's developer tools, **~$130M/year**. Full methodology…
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