"Research assistant for AI and ML papers, code, and methodology" matching MCP servers:

A local MCP server that integrates FastMCP with OpenAI to enable natural language queries for current weather, forecasts, and currency conversion using ExchangeRate-API and Open-Meteo.

Enables MCP clients to connect to a privacy-first, self-hostable workout planning and training log, allowing coaching agents to preview and apply program changes while accessing training data through OAuth-protected endpoints.

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 semantic search and conversational querying across a personal research library of PDFs, DOCX, and other documents using a vector database. It provides tools for document summarization, finding related papers, and high-accuracy retrieval for AI clients like Claude Desktop.

Enables comprehensive Google Meet and Calendar management through Google Calendar API v3 and Google Meet API v2, supporting calendar event creation with Meet links, space management, transcription, recording, attendance tracking, and meeting moderation controls.

An MCP server that automates code review by providing syntax checking, code explanation, and improvement suggestions using a multi-agent system.

Transforms AI assistants into a full ML engineering environment for training and fine-tuning models across multiple backends (local GPU, Mistral, Together AI, OpenAI) and cloud providers (Lambda Labs, RunPod, SSH-accessible VPS), with dataset management, experiment tracking, cost estimation, and deployment to Ollama/Open WebUI.

Enables agents to track ML experiments with a reproducible write contract, internal git snapshots, and a queryable knowledge graph, plus a live web UI for humans.

Enables AI-powered interaction with Metabase instances and PostgreSQL databases through natural language. Creates models, SQL queries, metrics, and dashboards using both Metabase API and direct database connections.

Enables read- or write-scoped access to synthetic enterprise orders, inventory, support tickets, and metrics via schema-validated JSON-RPC tools over stdio and HTTP, with permission allow-lists and audit logging. It also exposes enterprise resources and prompt templates for grounded, cited, multi-step assistant workflows.

Enables LLM clients to perform sandboxed file operations, manage persistent notes, fetch live weather and Wikipedia summaries, calculate arithmetic safely, and retrieve current UTC time via a standalone tool server over stdio or HTTP.

Enables researchers to search ArXiv papers, extract key insights (problems, methods, results) from abstracts, and perform ML-powered analysis including topic modeling, trend visualization, and author collaboration networks.

Enables LLM clients such as Cursor and Claude to discover and use generated REST services through MCP tools for create, update, and delete actions and MCP resources for list and get-by-id context. Services generated in one command are exposed simultaneously as REST endpoints and MCP tools and resources.

Enables AI assistants to interact with generated REST APIs by discovering and using CRUD tools and read-only data resources.

Enables AI assistants to discover and invoke CRUD tools and read-only resources over the Model Context Protocol for generated API services.

Enables MCP clients to count tokens and estimate costs, run BM25 search, compare model evaluations with paired bootstrap and McNemar tests, validate JSON Schema output, and scan for prompt injection and PII.

Enables comprehensive academic research by searching and analyzing papers from PubMed, Google Scholar, ArXiv, and JSTOR. Provides full-text access, citation management, and research organization through five powerful consolidated tools.

Enables AI assistants to interact directly with generated REST APIs by exposing CRUD operations as MCP tools and read-only data as resources.

Exposes generated CRUD services to LLM clients over the Model Context Protocol, providing tools for create, update, and delete actions plus read-only resources for listing and fetching records by id. It lets assistants like Claude, ChatGPT, and Cursor discover and manipulate the same data served by the project's REST endpoints without any extra glue code.

Exposes every generated CRUD service to LLM clients such as Claude, ChatGPT, or Cursor through discoverable MCP tools for create, update, and list actions plus read-only resources, so models can query and manipulate data without raw HTTP calls. It runs alongside the same codebase's REST endpoints, letting clients and AI share one data layer.

Enables LLMs to discover and invoke generated CRUD operations as MCP tools and read data through MCP resources, alongside standard REST endpoints, using one codebase.

Enables LLMs to discover and invoke generated CRUD tools and resources, allowing AI assistants to interact with the same data exposed by the REST API. Supports MCP clients such as Cursor and Claude over HTTP.

Enables LLMs to discover and invoke generated API actions as MCP tools and read context through MCP resources for list and get-by-id operations. It provides HTTP-based MCP access for clients such as Cursor and Claude without extra glue code.

Enables LLMs to interact directly with generated REST APIs by exposing CRUD actions as MCP tools and read-only data as MCP resources.

Unified academic paper search MCP server integrating OpenAlex, Semantic Scholar, CrossRef, and arXiv APIs, enabling Claude to search and analyze over 500 million scholarly papers.

Enables LLMs and MCP clients to discover and use generated CRUD services through MCP tools and resources, exposing actions like create, update, delete, and read-only list/get access alongside REST endpoints.

Enables AI assistants to discover and invoke generated CRUD tools and read-only resources, letting them interact directly with REST-backed services built on Express, Mongoose, or Sequelize.

Enables LLMs and AI assistants to discover and use tools and resources for CRUD operations on generated services, providing direct interaction with the same data exposed by REST endpoints.

Enables LLMs to discover and invoke CRUD tools and read-only resources for generated services, allowing direct interaction with the same data exposed by the REST API.

Enables LLMs to interact with generated REST API services by exposing MCP tools and resources for CRUD operations, listing, and retrieval. It lets AI clients discover and call create, update, delete, list, and get-by-id actions against the same data available over REST.