"mcp server fetch" matching MCP servers:

A Model Context Protocol server that integrates Cursor IDE with Bitbucket Cloud, allowing developers to fetch repository information and commit data directly from their Bitbucket workspace.

A simple MCP server that provides a tool to fetch current weather information for cities using the Open-Meteo API, communicating through stdin/stdout.

Enables AI models to access GitHub repository contents as context, with features to fetch entire repositories, specific file contents, and repository structures for use in AI interactions.

Federates UI component registries, enabling AI agents to fetch exact component code, dependencies, and setup prerequisites directly into the workspace. Includes sandboxed previews, anti-slop layout auditing, and offline-to-cloud telemetry sync.

Enables MCP clients such as Notion AI to execute native OpenCode file, search, shell, web-fetch, and todo tools with job management and permission approvals.

AI Swiss Army Knife — web fetch, search, screenshots, PDF extraction, code execution, and async task runner. One MCP server, 8 tools, 10x cheaper than alternatives.

An MCP server that provides data bridging from Spring Boot interfaces and a lightweight Python sandbox for script execution. It enables agents to fetch data as Markdown or Parquet files and perform automated data analysis within a controlled environment.

Facilitates LLMs to efficiently access and fetch structured documentation for packages in Go, Python, and NPM, enhancing software development with multi-language support and performance optimization.

A framework that provides search, fetch, and do primitives to enable AI agents to orchestrate complex tasks via sandboxed TypeScript code execution. It optimizes performance and security by reducing multiple sequential tool calls into a single, efficient inference round-trip.

A self-hosted MCP server that provides a single execute_code tool, enabling agents to write TypeScript to call multiple REST APIs via fetch() with transparent credential injection, reducing token usage by keeping intermediate results in the sandbox.

Extends VS Code Copilot with MCP tools for persistent semantic memory, code indexing and RAG search, sandboxed Python/shell execution, web fetch/search, dynamic tool management via a web UI, and structured multi-step reasoning.

Backtest PineScript v6 strategies locally from your AI agent: transpiles Pine to C++ and runs it on the deterministic, TradingView-validated PineForge engine via Docker. Tools for single backtests, parameter-grid sweeps, and Binance OHLCV fetch — no API key, data never leaves your machine.

MCP server that exposes Ansible modules to AI agents with a search-schema-check-execute workflow, enabling safe ad-hoc runs via check mode by default. It provides tools to search modules, fetch schemas, run modules, and list collections.

Enables AI agents and CLI users to drive Kaggle notebooks end-to-end—pull, edit, save new versions, run, monitor, and fetch logs—using a local stdio MCP server with a Kaggle API token.

Enables LLMs to drive Blender through socket-based two-way communication, letting them create and modify 3D objects, apply materials, run Blender Python, and fetch or generate assets while visually verifying results. Supports prompt-assisted 3D modeling, scene creation, and manipulation.

Enables AI agents to fetch pinned GitHub sources, prepare per-job project environments, run declared commands, and verify results through file, JSON, and stdout checks while returning only compact status plus local evidence paths. Supports long-running Windows CLI and repository tasks with resumable jobs, background execution, and measured token and time savings.

Enables Claude Code and Cowork to delegate coding tasks over Remote MCP to isolated Cloudflare Container sandboxes that push work branches and open Draft PRs, with tools for task polling, steering, cancellation, and optional web search and fetch. It authenticates users through a GitHub App and supports long-running task status, results, and budget controls.

Enables agents to discover and run any OpenAPI-described API through a generated typescript-fetch SDK, using just two tools: search to find relevant SDK methods with their signatures, types, auth requirements and runnable examples, and execute to run agent-written TypeScript that composes real SDK calls in an isolated sandbox and returns only the compact result. Keeps large APIs out of the agent's context and secrets out of reach, while the SDK itself stays an ordinary package.