"A server for searching academic papers and retrieving metadata" matching MCP servers:

Enables comprehensive Salesforce metadata management from MCP clients, providing 221 tools for creating objects, fields, Apex, flows, LWCs, Agentforce agents, and more.

Enables extraction and querying of database schema metadata from PostgreSQL, Snowflake, SQL Server, BigQuery, and Oracle databases, saving results locally for analysis.

Relays a result screenshot to Slack so you can review a coding agent's output without opening the app.

Enables coding agents to add a bounded semantic-judgment layer for routing, ranking, extraction, verification, and escalation, returning typed signals and review recommendations.

A MCP (Model Context Protocol) server for fetching HuggingFace daily papers.

Provides over 1,000 creative ways to decline requests across four categories (polite, humorous, professional, and creative). The MCP server wraps a REST API to help users craft professional rejections through natural language interactions.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

Enables Codex to consult Claude Code for a second opinion through a local stdio MCP server, exposing the ask_claude tool for text-only reviews and recommendations using existing Claude Code authentication. Codex can request findings and tradeoff analysis without allowing Claude to edit files or run commands.

Use Codex as a subagent inside Claude Code: async dispatch into a long-lived codex app-server, results pushed back into the Claude session automatically, steer/interrupt mid-task, and Codex's built-in code review.

Odds are, most of what you want to build is already open source. Tell your AI agent what you're making, and it finds it: which repos to use as they are, which to adapt, and what's left for you to write. Every repo it names is real, licensed for your use or flagged, and checked live.

Enables AI coding agents to discover and verify current open-source libraries and models using live GitHub activity, PyPI adoption, license checks, and hardware VRAM estimates before generating code.

A CDP-native MCP server that lets AI agents drive a real Chrome browser with deep network/console inspection, robust page interaction, and the ability to reverse-engineer a site's API flow into runnable code with dependency chaining and replay verification.

Enables AI agents to break down complex tasks into manageable pieces using a structured JSON format with task tracking, context preservation, and progress monitoring capabilities.

A live MCP server that connects AI assistants to a running Obsidian instance, enabling full app automation including reading notes, clicking UI elements, managing plugins, and executing JavaScript.

Automatically crawls documentation websites, converts them to organized markdown files, and generates condensed cheat sheets. Intelligently categorizes content into tools/APIs and provides local-first access to downloaded documentation.

A Model Context Protocol server that helps programmers understand code by providing explanations, tech stack analysis, and best practice suggestions through prompt templates.

An AI-centric MCP server that enables automated Xilinx Vivado workflows, including project management, synthesis, implementation, and timing analysis. It allows AI agents to drive hardware design processes while integrating directly with the official Vivado GUI for visual context.

MCP server that launches Mesen/MesenCE headlessly and exposes the emulator's debugger-face Lua API over MCP JSON-RPC, enabling AI agents to load ROMs, step frames, inspect memory/registers, manage breakpoints/watches, trace execution, and export Code/Data Logger maps for SNES, NES, PC Engine, and Game Boy Advance.

Exposes GroupDocs.Metadata as AI-callable tools for reading, searching, writing, and removing metadata in documents.

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.

Provides AI agents with ground-truth knowledge of Strapi projects by reading real schema files, enabling accurate queries and safe refactoring operations.

Enables Codex and other MCP clients to read kickoff cards, acceptance criteria, Git evidence, and next steps, then append validated, audited progress events for resumable project handoffs.

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

Enables OpenAI-compatible clients to access external tools, APIs, repositories, and data sources through MCP with allowlisting, approval, and argument validation, deployable as a FastAPI service or Python library.

Enables agents to read a project's kickoff card, acceptance criteria, AGENTS rules, handoff package, progress, Git trail, and next step, and to append validated, audited progress events with matching Git commits through short-lived scoped credentials.

Enables AI agents to retrieve Git-backed project context, handoff packages, progress, and next steps, and to append validated progress events with matching project-record commits. This supports visible agent progress, resumable work, and safe handoffs while keeping high-risk actions outside the MCP surface.

A local, offline MCP server that enables AI agents to collaborate with SketchUp for controlled modeling, editing, and review tasks via 41 MCP tools.

Enables agents to read Git-backed project context and append audited, idempotent progress events through scoped, short-lived credentials, supporting resumable handoffs and visible project state.

Enables AI agents and users to verify claims about Jira issues and issue counts against live Jira, retrieve verifiable facts for drafting updates, and re-check previously posted claims. It provides read-only tools and an issue panel so status updates can be evidence-based and mismatches corrected before or after posting.

Enables MCP-compatible AI clients to inspect Godot project and editor state, observe opted-in running scenes, manage processes, and apply permission-scoped guarded edits. It starts read-only and exposes additional capabilities only through explicit command-line flags.