"Forwarding Work Requests and Comments to Other Models or Tools" matching MCP servers:

Enables searching, comparing, and inspecting AI models by pricing, context window, and capabilities via the models.dev catalog.

Web Content Retrieval (full webpage, filtered content, or Markdown-converted), Custom User-Agent, Multi-HTTP Method Support (GET/POST/PUT/DELETE/PATCH), LLM-Controlled Request Headers, LLM-Accessible Response Headers, and more.

Provides comprehensive read/write access to Word documents, including comments, track changes, and reply threads.

Classifies development task complexity (LIGHT/MEDIUM/HEAVY) and recommends the most cost-efficient AI model per provider, enabling optimized model selection for coding tasks.

Enables AI agents to query OpenRouter model information including prices, ELO rankings, context, and perform comparisons.

Exposes a read_source tool that lets agents read cited tickets, docs, and files through the same path the checker verifies, so quoted business rules match their sources and stay in sync as those sources or their authors change. It also keeps undecided product decisions explicit as open questions rather than letting agents invent answers.

Enables MCP-compatible AI clients to download comments from YouTube videos, Shorts, live streams, playlists, channels, and community posts; check and list downloads; retrieve files in JSON, CSV, HTML, XLSX, TXT, or ZIP; and get video transcripts and channel details.

MCP server that provides OpenRouter model pricing data, enabling price lookups, trending/cheapest lists, and model searches without an API key.

Enables adding inline, range-anchored comments to specific text fragments in Google Docs, overcoming the limitation that Google APIs cannot create anchored comments.

Turns product ideas into rendered UI screen mockups and a buildable component manifest, so downstream agents can develop real screens from the generated designs.

Enables coding agents to run persistent, budgeted work sessions with dependency graphs, human decision queues, required validation, and clean handoffs for autonomous or human-in-the-loop execution.

Enables retrieval of comments from Bilibili videos and dynamic posts with support for pagination, sorting, and nested replies in both Markdown and JSON formats.

MCP server that fetches and replies to Figma file comments, with filtering and a triage skill to organize them into decisions, questions, and to-dos.

Scans a repository for maintenance toil (flaky tests, expiring certificates, TODO rot, dead flags, etc.) and returns a prioritized queue with a credit ledger to track who cleared what.

MCP server that lists available GitHub Models and compares responses from multiple models for the same prompt.

An MCP server that wraps the @apitomy/data-models library for querying, validating, and editing OpenAPI and AsyncAPI documents.

Provides live wait times and office details for all 60 Oregon DMV field offices by scraping the ODOT website, with no authentication required.

A local kanban board and docs tool that AI agents can drive over MCP. It enables project management without accounts or API keys.

An MCP server that lets Claude read Figma file comments directly, with tools to list comment threads scoped to a frame or file and render images of pinned nodes.

Enables MCP clients to read every comment and reply in a Figma file—including authors, timestamps, resolution state, and pin metadata—and to post new comments, threaded replies, or frame-pinned comments via the official Figma REST API. It authenticates locally with a personal access token and never alters or deletes designs.

Provides a local-first AI workspace for chatting, invoking skills, managing projects, and integrating MCP tools, with all data stored on the user's machine.

Enables AI agents to search and read a local, provider-independent email archive, reconstruct contacts and interactions, and prepare draft responses without sending anything.

Enables reading and acting on comments in Figma and FigJam, resolving which layer each comment is pinned to, complementing the official Figma MCP server.

Enables MCP clients to read and filter Figma comment threads, post and reply to comments, manage reactions, and delete comments in accessible Figma files.

A lightweight MCP server that adds Figma Comments support to AI assistants, enabling reading, querying, and replying to comments via the Figma REST API.

Enables AI agents to look up facts about notable machine-learning models — organization, release date, parameter count, training-compute (FLOP) estimate, hardware and accessibility — by name, and to search or aggregate them by organization, domain, year, or compute threshold. Supports filtering the dataset newest-first and producing per-year training-compute trends, keylessly and without authentication.

Enables users to choose among seven decision-making frameworks and work through them step by step with their own situation, ending with one clear decision and a next action. It also chains multiple frameworks for larger decisions and provides model guides and recommendations.

An MCP server for time tracking and billing that allows freelancers to control timers, manage projects, and export invoices via natural language in any MCP-compatible AI assistant.

An MCP server that helps interact with and compare different GitHub Models, enabling listing, filtering, and comparing model responses.

A comment-based spec-driven development MCP server that facilitates workflows from requirements creation to implementation via AI, using @spec-impl markers.