Librarian MCP

Librarian MCP

A Model Context Protocol server providing pre-curated canonical memory, prose/code provenance checking, and benchmark metrics to improve accuracy and reduce costs across AI tools.

Category
Visit Server

README

Librarian MCP

pip install librarian-mcp

PyPI version License: AGPL-3.0 Pledged Commons CI GitHub stars

A real, measured alternative to "bigger context windows." Pre-curated canonical memory + prose/code provenance checking + benchmark metrics, delivered as a Model Context Protocol server that works across Claude Code, Cursor, VSCode (via Continue), and any MCP-capable client.

Try it without installing →

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 in the R9 Empirical Test Companion Paper.

librarian_context — Intent API

librarian_context(intent="outreach", max_tokens=16000)
Intent What it loads Approx. tokens
"" (default) Base R9-v2 preload only ~4,500
"canonical" Base + canonical values + canonical laws ~15,000
"outreach" Base + canonical + Opening Gambit + letter queue + Cephas + Glass Door + Witness ~30,000
"architecture" Base + canonical + Pledge + IP split + Medallion + Pedestal Stake ~20,000
"founder_voice" Base + Rhetorical Keystones + Pine Books + Anachronism + Cloyd + Three-clock ~10,000
"benchmark" Base + R10 results + R9 brief + 75-Q bank + rubric + posture disclosure ~10,000
"operational" Union of outreach + canonical ~30,000

List inputs for union queries: intent='["benchmark", "founder_voice"]'

Returns:

{
  "packet": "...markdown...",
  "sections_included": ["r9v2_base.md", "canonical/canonical_values.yaml", ...],
  "token_count": 14832,
  "source_version": "a1b2c3d4e5f6",
  "truncation_note": null
}

metrics_summary — Schema

{
  "total_calls": 1200,
  "per_vendor": {
    "anthropic": {
      "calls": 600,
      "hot_accuracy": 95.3,
      "cold_baseline_est": 8.2,
      "dollars_saved_est": 42.17,
      "cache_hit_rate": 50.0
    }
  },
  "per_model": {
    "claude-haiku-4-5-20251001": { "..." : "..." }
  },
  "cumulative_hot_accuracy": 94.8,
  "cumulative_cold_baseline_est": 8.7,
  "cumulative_dollars_saved_est": 127.50,
  "opt_in_share": false,
  "since": "all_time"
}

Pricing

Tier Who it's for Price
Pledged Commons Any nonprofit, cooperative, academic institution, or public-service organization with IRS-verified EIN (or international equivalent) $0 forever. Full feature set. Under the Cooperative Defensive Patent Pledge.
Individual Single developer $0 (community edition, this repo) for local use; $15/mo for hosted multi-repo context + team sharing
Team 2–50 seats $10/seat/mo (min $50)
Enterprise 50+ seats, custom canonical schemas, audit logs, SAML, support Contact. Typically $50–100/seat/mo.

The commercial tiers pay for the commons. No grant funding, no VC, no extractive margin. Cost+20% on operating expense. That's it.

Why MCP (not a Cursor extension)

Because you shouldn't have to pick between your AI assistants. MCP servers work across Claude Code, Cursor (v0.45+), Continue (VSCode / JetBrains), Zed, and every MCP-capable client in the roadmap. One server, all your tools.

Install

Quick start (local, Python 3.10+)

git clone https://github.com/liana-banyan/librarian-mcp.git
cd librarian-mcp
pip install -e .
librarian-mcp  # starts on stdio for MCP clients

With optional dependencies

pip install -e ".[all]"   # tiktoken (accurate token counts) + anthropic + pyyaml
pip install -e ".[dev]"   # + pytest, ruff, mypy for development

Claude Code

claude mcp add librarian python -m librarian_mcp

Cursor

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "librarian": {
      "command": "python",
      "args": ["-m", "librarian_mcp"]
    }
  }
}

Continue (VSCode / JetBrains)

See docs/continue-integration.md.

Development

pip install -e ".[dev,all]"
ruff check src/ tests/          # lint
mypy --strict src/librarian_mcp/  # type check
pytest -v                        # test (34 tests)

Status

April 21, 2026 — v0.2.0. Intent-aware librarian_context live with bundled preload (R10-validated). Benchmark metrics recording live. Prose Provenance tool upgraded to v0.2.0. PyPI name librarian-mcp reserved. CI/CD staged.

License

AGPL-3.0. Commercial licensing for the paid tiers is a separate agreement; the Pledged Commons tier is covered by AGPL + the Cooperative Defensive Patent Pledge.

Contact

  • General: hello@liana-banyan.com
  • Enterprise: enterprise@liana-banyan.com
  • Press / AI policy / datacenter-alternative questions: press@liana-banyan.com
  • Founder: Jonathan Jones, Founder & General Manager, Liana Banyan Corporation (Wyoming C-Corp)

Contributing

We welcome contributions — code, corpus preloads, benchmark replications, and research extensions.

  • BOUNTIES.md — paid bounties for specific contributions, from $25 good-first-bounty issues to $500 deep bounties
  • BUILDING_TOGETHER.md — guide to running, extending, and contributing back upstream

"You build the Features — We're building the Board."

Pledged into the commons. For the Keep.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured