"Eight" matching MCP servers:

Stop hallucinating numbers. An MCP server that runs every math expression through four independent numeric engines in parallel and returns all four results side by side — so when they disagree, you know the answer is numerically unreliable.

Enables Northwestern University AI students to sign in to their class OpenRouter workspace and generate, edit, and locally re-mask images through eight account, model, and image tools. Saved files, prompts, costs, and inline previews are kept local, with workspace-scoped credentials separated by cohort.

The Ritwik Joshi Personal MCP Server is a standardized Model Context Protocol (MCP) data source designed to give AI agents direct, authoritative tool access to his professional profile. It features eight tools that enable AI assistants to fetch real-time information regarding his speaker profile, keynotes, TEDx talks, blog articles, and booking details without relying on outdated data.

A local MCP server for generative image description, providing prose captions, OCR, and LoRA dataset caption sidecars via Florence-2, with deterministic decoding and an honesty contract.

Official remote MCP server for Lobby, the bilingual AI receptionist. Five free no-auth tools: business phone scripts and IVR menus (English + Mexican Spanish), ElevenLabs agent system prompts, missed-call cost math, and a hire-a-receptionist verdict.

Enables AI agents to search, filter, and monitor curated AI model API free credits and low-cost token promotions through eight tools, four resources, and three prompts — including finding deals expiring soon, checking eligibility and blocked conditions, and producing daily briefs.

Enables Alexa+ agents and other MCP hosts to analyze dry exam/card pulse text from a mock trading desk, returning structured test results, signals, problems, needs, alarms, and recommendations over Streamable HTTP. It works with an offline rules engine and optional Nebius Token Factory enrichment.

An MCP server for supply chain intelligence that enables conversational querying of eight operational KPIs and anomaly detection data through Claude. It allows users to analyze performance metrics like OTIF and inventory turnover while providing LLM-generated root-cause explanations.

Enables screen-free family play through a voice assistant by secretly generating a solvable whodunnit set in the household's own named rooms, or locking in a yes/no guessing target, and revealing answers only through governed tools. Keeps every culprit, clue, and secret in server-side SQLite, enforcing question counts and accusation limits so the spoken host can narrate without knowing the solution.

Enables agents and people to commission an AI painter via MCP by submitting a description or photo, then check the commission's status and view recent paintings.

A powerful MCP server that brings AI vision capabilities to Claude Desktop. Analyze images and videos using OpenAI GPT-4o, Claude, or any compatible vision API.

Enables MCP clients to generate a safe, age-appropriate first-night plan for a newly placed foster child, including screened shows, films, music, nearby restaurant options, and per-item safety checks based on age, triggers, and first-night themes.

An MCP server that autonomously optimizes ONNX ML models for Arm64 deployment, providing tools to analyze models, apply real INT8 quantization, benchmark performance, and generate Arm64-optimized Docker deployment packages.

Gives an Alexa+ voice agent a household memory layer that enforces allergy rules, grounds answers to a child's reading level, and checks existing ports and spare cables before any purchase. It runs as a self-hosted Streamable HTTP MCP server with eight tools, including Bedrock-powered multimodal inspection of unboxed deliveries.

Provides tools for AI assistants to search and explore the Update Night catalog of AI development tools, including agent frameworks, skills, and MCP servers. Enables querying entries by category, fetching detailed entries, and browsing recent news.

Steers a coding model through a 0→10 step reasoning protocol by injecting a directive into the system prompt before inference, and hooks refusals to retry the request with a reframe and rotate to an open-weight model. Exposes tools, prompts, and resources for traffic-light state, history, health, and configuration over stdio, Streamable HTTP, or a Cloudflare Worker.