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.
README
Librarian MCP
pip install 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 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 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-bountyissues 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
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
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.
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.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
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.
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.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
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.