tailor-mcp 9.1.0

Tailor: a local-first MCP framework that lets any MCP-speaking AI work with your own data — your Wardrobe stays on your machine, with server-side analytics, consent gates, cost gates, and a durable audit log.

tailor-mcp

Local data preprocessing for AI — structured summaries, governed access, auditable answers.

Pasting raw data into an LLM is expensive, often produces worse answers (the model spends capacity extracting numbers from text instead of reasoning), and at scale is simply impossible. A 16-subject force-plate cohort comparison is 769,311 tokens of CSV — that exceeds Claude's 200K context window. You cannot ask the question at all without chunking, streaming, or some other orchestration workaround.

Tailor computes the answer on your machine and returns 820 tokens. The result is identical; the question becomes answerable in a single call. Your data never leaves your machine, and every action is recorded in a local SQLite audit log.

Tailor is a local MCP server that sits between an LLM client (Claude Desktop, Cline, Cursor, or a local model via Ollama) and any structured source: directories of per-subject CSVs, MATLAB binary exports, REDCap exports, running data, or anything you register through a small extension point.

The numbers

Measured, reproducible benchmark — force-plate cohort fixtures, tiktoken cl100k_base:

Scenario	Raw → LLM	Through Tailor	Reduction
Single analytical question, 1 subject	48,006 tokens	73 tokens	657×
16-subject cohort comparison	769,311 tokens (exceeds the 200K window)	820 tokens	938×
Resuming a 5-session analytical thread	771,743 tokens	2,427 tokens	318×

Results are identical to processing the raw stream — the computation happens server-side. Over a 5-session analytical thread at Claude Sonnet 4.6 input pricing, that difference is roughly $11.58 vs $0.04.

Full methodology, assumptions, and a prompt-caching counter-factual: https://github.com/saahasmuthineni/tailor-mcp/blob/main/benchmarks/token_efficiency.md

Install

Prerequisites:

1. Claude Desktop (Windows: Microsoft Store; macOS: claude.ai/download)

2. uv — the installer Tailor uses:

   # Windows (PowerShell)
   powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
   
   # macOS / Linux
   curl -LsSf https://astral.sh/uv/install.sh | sh
   

Install and run:

uv tool install tailor-mcp
tailor pilot

tailor pilot runs a short setup wizard, registers Tailor with Claude Desktop, and configures your first data source (CSV / MATLAB / REDCap). Fully quit and reopen Claude Desktop (system-tray Exit on Windows; Cmd+Q on macOS), then ask a question about your data — Claude calls a tool, the computation runs locally, and you get back per-group statistics. Nothing leaves your machine.

The bundled demo fixtures (S001–S016) are synthetic by construction — random-walk traces sized to mimic real cohort shapes, not real participant data.

How it works

LLM client <--> RouterMCP (validate -> circuit break -> consent -> cost
                           -> execute -> scrub -> audit + provenance)
                   |                  \            \
              ChildMCP                 VaultLayer    LocalLLMLayer
   (one per data source:               (cross-session  (optional local-LLM
    CSV dir, MATLAB, REDCap,           analytical       guardian; opt-in)
    running data, your own)            memory)

Every tool call passes through a server-side pipeline the LLM cannot bypass: parameter validation, a circuit breaker, a per-domain consent gate, a token cost gate, a PHI/sensitive-data scrubber seam (no-op by default; subclass per child for institutional policy), and an audit log. Every successful result carries a _meta provenance stamp — package version, UTC timestamp, domain, tier, scrubber id, token counts — minimum-viable provenance for results that may end up in a report or paper.

Three-tier access model

Data minimization is enforced server-side, not in the prompt. The LLM cannot escalate to higher-resolution data without explicit user approval.

Tier	What the LLM sees	Typical tokens	Gate
1 — Free	Server-computed reports: summaries, stats, trends, anomalies	200 – 1,500	None
2 — Consent	Downsampled streams at 5–30 s resolution	3,000 – 7,000	Domain consent
3 — Cost	Full per-timestamp streams with precision reduction	25,000 – 60,000	Consent + cost approval

Most analytical questions resolve at Tier 1 — zero raw data leaves the machine, and the freed context goes to reasoning rather than data shuffling.

Data sources shipped today

• csv_dir — a local directory of per-subject CSV files; cohort summary + a force-decline fatigue diagnostic
• matlab_file — MATLAB .mat binary exports (v5/v6/v7.2; requires the [matlab] extra)
• redcap — REDCap CSV/JSON exports with built-in PHI scrubbing driven by the project_metadata.csv data dictionary
• running — a Strava API wrapper, shipped as a worked example of the extension pattern, not as the headline use case
• template — a runnable starting point: copy, rename, wrap your own source

Adding a source means copying children/template/ and implementing five things (domain / display_name, consent_info, tool_definitions with tiers, execute(), estimate_cost()); your source inherits the full governance pipeline. children/csv_dir/ is a complete second worked example.

Who it's for

Good fit: researchers and RSEs building LLM-assisted analysis where data governance, audit trails, or reproducibility matter; teams wiring structured sources into Claude Desktop or any MCP client and wanting server-side computation over raw-data prompts; anyone who needs a local-first setup.

Not a good fit: clinical decision-support or regulatory-compliance deployments (this is research infrastructure, not a validated clinical tool); hosted/cloud workflows (the architecture is deliberately local-first); projects requiring an independent security audit (solo-maintainer project, no external review yet).

Status

Validated on Windows 11 (Microsoft Store Claude Desktop) and macOS. Cross-client round-trip confirmed with Cline; any MCP-compliant client works without bespoke accommodation. CI matrix: Ubuntu · Windows · macOS × Python 3.10–3.12. Community validation is ongoing — issues and reports welcome.

Project

• Source code: https://github.com/saahasmuthineni/tailor-mcp
• Benchmark methodology: https://github.com/saahasmuthineni/tailor-mcp/blob/main/benchmarks/token_efficiency.md
• Architecture Decision Records: https://github.com/saahasmuthineni/tailor-mcp/tree/main/docs/adr
• Issues: https://github.com/saahasmuthineni/tailor-mcp/issues

License

AGPL-3.0-or-later (v9.0.0 onward; releases through v8.0.0 remain Apache-2.0 for prior recipients). For local-first use — the framework's primary deployment shape — the AGPL network-trigger clause rarely fires, so it adds minimal friction for individual researchers and institutional installs. It exists as a structural lever against extractive cloud reuse: a hosted "Tailor as a service" fork must publish its modifications.