"How to Read and Write Data with a Discord Server" matching MCP servers:

A Model Context Protocol server that enables AI agents to access, search, and understand structured package documentation and source code from Git repositories. It automatically generates specialized tools to browse module overviews, components, and detailed documentation for technical libraries.

A local MCP server that lets LLM agents read EPUB books and PDF documents with outline-first navigation, precise node reads, and local SQLite persistence.

Enables PDF reading through MCP with text extraction, Chinese OCR for scanned documents, page rendering, and keyword file search from any compatible MCP client.

Enables an MCP client to retrieve explicitly allowlisted Markdown context, create and read local task contracts, write local handoffs, and return locally configured routing metadata without invoking a model, executing commands, or writing to the vault.

Enables AI agents and MCP clients to fetch webpages, strip noise, and convert them into clean, token-efficient Markdown. Supports optional crawling, caching, robots.txt compliance, and link preservation.

Gives AI assistants precise, read-only access to Discord's developer documentation, including keyword search with identifier-aware ranking, full section retrieval, and structured lookups of REST endpoints, objects and enums. It also reports changelog entries and detected documentation changes since a given date, with every answer pinned to the indexed docs commit.

MCP server to read protected (or unprotected) PDF files, extracting text to a temp file, and handling large PDFs.

Enables AI agents and LLM pipelines to fetch and crawl websites locally, extract readable content, and convert pages into clean Markdown with preserved links while minimizing token usage.

Enables converting a public release-note URL into ordered upgrade tasks with source excerpts, anchors, confidence, and uncertainty, without executing release text or project code.

A Model Context Protocol server that converts Markdown documents to plain text with flexible options and dual protocol support.

Enables users to turn any API's documentation into a tested MCP server whose tools expose the API's own operations, with linting, contract tests, and live verification. It accepts OpenAPI/Swagger, Postman, RAML, WSDL, GraphQL, RSS, and HTML docs.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

A Model Context Protocol (MCP) server that coordinates AI agents to transform database tables into Wikipedia-style data dictionaries.

Converts Markdown files to Atlassian Document Format and publishes them to Confluence, with support for rendering Mermaid diagrams as PNG attachments.

Converts any OpenAPI/Swagger spec into an MCP server, exposing REST API endpoints as tools for Claude.

Enables converting Markdown text into clean HTML for headings, lists, code blocks, tables, links, and images, with optional full-document wrapping via x402 micropayments.

A minimal MCP server that auto-generates tools from your OpenAPI spec, optimized for Vercel.

A Cloudflare Worker that transforms Cloudflare AI Search (AutoRAG) instances into an MCP server for querying documentation. It enables AI models to search and retrieve relevant information from custom document sets stored in R2 buckets.

Enables local conversion of PDFs to Markdown, with automatic OCR fallback for scanned pages, image extraction, and page-by-page structure preservation.

Enables querying Polish data protection regulations, decisions, and guidelines from the UODO directly from AI assistants.

Provides a single-writer MCP server for a governance-grade knowledge base of markdown documents with version control and query capabilities.

Fetches website content and converts it to Markdown format with AI-powered content cleanup, ad removal, and full OpenAPI/Swagger specification support for easy processing by AI assistants.

Enables AI coding agents to triage BigQuery ML and BigQuery AI tasks, look up exact model and function syntax, and search 137 syntax cards, 14 reference guides, and 104 live-verified narratives via a zero-dependency stdio MCP interface.

Converts Markdown files and raw content into professionally styled PDFs with full support for Mermaid diagrams and syntax highlighting. It offers customizable page formats, margins, and modern typography for high-quality document generation.

Provides LLMs with real-time access to up-to-date documentation from PyPI, npm, crates.io, GoDocs, DockerHub, GitHub, and GCP, preventing outdated code generation and API hallucinations.

Enables AI assistants to search, fetch, and quote live motorcycle repair guides and gear reviews, browse the latest posts, and list available topics through four MCP tools. Data is pulled from the site's WordPress REST API with a six-hour cache, so newly published articles appear automatically without redeploying.

Enables querying documents through a Langflow backend using natural language questions, providing an interface to interact with Langflow document Q\&A flows.

A Python-based MCP server that enables document-based question answering by processing PDF, TXT, and Markdown files through OpenAI's API. It provides hallucination-free responses based strictly on document content using semantic search and includes a web interface for management.

Enables document-based question answering using OpenAI's GPT-4 with semantic search and embeddings. Upload PDF, TXT, or Markdown files and get answers strictly based on document content with source attribution and confidence scores.

An HTTP MCP server that indexes large documents into exact-line-numbered sections, enabling AI models to locate, read, summarize, and edit parts of a document without ingesting the whole file.

Enables AI assistants to analyze project folders for dependencies and frameworks and fetch current documentation or datasheets from the web.

Enables hybrid search (BM25 + semantic with RRF) over documentation, with Thai language support. It exposes MCP tools for adding documents and searching via Qdrant and Ollama/OpenAI, using a stateless Streamable HTTP transport.

A multi-server system that converts PDF documents to Markdown format using FastMCP architecture with upload and convert servers orchestrated by a reactive client agent.

Provides Large Language Models with real-time access to the latest documentation for Python libraries like Langchain, LlamaIndex, and OpenAI, enabling accurate and up-to-date code suggestions.

Enables AI assistants to search and retrieve documentation, component specs, theme guides, and CLI commands for the Meta Astryx Design System, providing structured tools and guidelines for coding agents.

Converts vector PDF circuit schematics into evidence-preserving SchematicIR JSON with tools to inspect, convert, and validate schematic PDFs.

Enables comprehensive local file system operations including searching and reading content from various formats like PDF, Office documents, CSV, and text files. It features automatic character encoding detection and secure path validation for directory access.

Converts documents (PDF, Word, PPT, images, HTML) to Markdown via MinerU API. Supports URL and local file inputs with OCR, formula/table recognition, and automatic large file handling.

A Model Context Protocol server that enables listing and reading PDF documents from a configured directory, extracting text content for MCP clients like Claude Desktop.

Enables interactive AI chat through the Anthropic API, with document retrieval, slash commands, and extensible MCP tool integrations.