frameforge-mcp

frameforge-mcp

MCP server that renders AI-authored documents to images and returns typed diagnostics for self-correction. It also provides visual QA metrics, raster-to-vector reconstruction, and image-to-draft proposals.

Category
Visit Server

README

frameforge-mcp

The FrameForge MCP server: the agent-facing surface of the FrameForge family. An AI agent authors a document with the SDK, this server renders it, and the agent verifies against the pixels that came back.

pip install frameforge-mcp
frameforge-mcp                      # stdio MCP server
frameforge-live                     # local web UI over the same loop

Unlike frameforge-api (the contract) and frameforge-sdk (authoring), this package is not a leaf and does not pretend to be one. It sits at the top of the stack and depends on the engine, because verification needs something that can actually render.

Two packages, one distribution

package what it is imports MCP?
frameforge_mcp the server, its ~33 registered tools, and (nested) live, the local feedback-session web UI
frameforge_coach the Vector Construction Coach — style-as-grammar, layer-order discipline, the silhouette gate no

The raster→vector lane is not here: frameforge_vision is its own distribution, usable without a server or an authoring SDK. This package depends on it (pip install 'frameforge-mcp[vision]') rather than shipping a second copy of the same module name.

live is nested inside frameforge_mcp because it genuinely is one of the server's surfaces: it imports frameforge_mcp.server directly and exists to put a browser in front of the same validate/render loop.

coach is deliberately not nested. It does not import the MCP, and 6 tests plus 10 examples reach it directly — routing them through a server package would assert a dependency that does not exist.

What it does

  • Author → render: run SDK code or FrameForge YAML, get validation issues plus a PNG back in one call.

  • Verification signals: every render result carries the engine's typed diagnostics — text fit, layout overflow, ink collisions, legibility (type below the legible floor, WCAG contrast), paint intent, design-token census. A render that succeeded but produced something unreadable says so.

  • Portable font evidence: run_sdk_code, run_sdk_client, render_frameforge_yaml, and fit_text accept a .fp font_closure plus optional font_generics. Validation and rendering share the same strict provider and report metrics_mode: closure with the closure SHA-256.

  • Visual QA: real NCC/RMSE/MAE metrics between a reference and a candidate.

  • Raster → vector: a coordinate-aware reconstruction workspace — measure, mark, overlay, fit primitives, vectorize, score.

  • Image → draft: propose documents from images, PDFs, or SVGs. Every proposal is unverified CV/VLM output and is round-tripped through a render before it is shown.

  • Deprecated forms: list_deprecated_forms returns the contract's registry — what each retired spelling became, and whether it still validates — and migrate_deprecated_forms rewrites them, returning the migrated YAML with apply: true.

    These run before a render, and that ordering is the point. Two of the deprecated forms (the pre-P3 inline stroke bundle, the pre-P4 size object) are rejected by the contract, so a document carrying one can never reach render_frameforge_yaml at all — an agent holding it gets "does not validate" and no route forward. The rewrite is mechanical, so this is that route. Neither tool renders, and neither touches a session; the migration does not mutate its input and is idempotent.

PALS's Law

Every tool in this server treats model output as untrusted. Proposals are labelled unverified, estimate-mode measurements name themselves as estimates, and a signal the server cannot resolve is reported as unresolved rather than scored as a pass. That is the point of the render loop: an agent that cannot see its own output cannot correct it.

Optional extras

pip install 'frameforge-mcp[vision]'   # the frameforge-vision lane: measure, vectorize, propose
pip install 'frameforge-mcp[vlm]'      # local CPU vision-language describer
pip install 'frameforge-mcp[pdf]'      # PDF input for propose_from_document
pip install 'frameforge-mcp[browser]'  # headless Chromium raster

The base install includes frameforge-sdk[metrics] and renders through CairoSVG, so closure metrics and browser-free visual verification work without another extra.

Provenance

Since the 2026-08-01 cutover this repository is the MCP source of truth. The former extraction script is historical; the monorepo depends on this package in its dev group and no longer owns a second MCP implementation.

See MIGRATION.md and examples/font_closure_tool_call.json for the portable closure call shape.

Licence

MIT — see LICENSE.

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
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
Qdrant Server

Qdrant Server

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

Official
Featured