"A tool or service for YouTube script writing assistance" matching MCP servers:

A tool discovery MCP server that integrates with Azure DevOps wikis and code-graph-rag to match natural language goals to tools, enabling tool composition and proxy testing.

A Model Context Protocol implementation that enables AI assistants to interact with markdown documentation files, providing capabilities for document management, metadata handling, search, and documentation health analysis.

Enables AI agents to retrieve free, source-linked decision methods and optional decision packs through MCP tools such as catalog, method, sample_pack, and pack_offer. It operates without model calls and disables payments by default.

A self-synthesizing AI knowledge wiki that provides a read-only MCP surface for external agents to trace the wiki content and graph.

An MCP server that enables generating scripts based on specified topics and keywords, while also providing functionality to store and summarize notes.

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.

Enables querying context about the MAM product components and architecture through MCP, allowing agents to retrieve targeted product information on demand instead of loading full documentation.

A Bun-based MCP server that allows AI models to query Swagger/OpenAPI documentation from local files or remote URLs. It enables users to search for APIs, retrieve detailed endpoint definitions, and fetch schemas to facilitate code generation and API integration.

An MCP server that helps transfer C4 diagrams from DrawIO and PlantUML into a DocHub architecture repository.

Provides accurate Pine Script v6 coding assistance through search, completion, and reference lookup tools using a comprehensive Japanese manual database. Enables developers to quickly find functions, constants, annotations, and API specifications for TradingView Pine Script development.

Optimizes EDT MCP tool descriptions by replacing verbose documentation with concise summaries stored in SQLite, reducing context usage while allowing on-demand retrieval of full tool guides.

Provides AI assistants with specialized tools to interact with NIST's Open Security Controls Assessment Language (OSCAL) framework. It enables agents to retrieve schemas, explore models, and generate valid OSCAL documentation for security compliance automation.

Provides AI assistants with locally bundled tools to explore NIST OSCAL models, retrieve schemas, list community resources, and generate valid OSCAL templates for governance, risk, and compliance workflows.

Enables AI assistants to professionally edit Persian text by reading a draft, diagnosing its problems, and assembling a tailored checklist from a 1,577-note editorial knowledge base spanning orthography, punctuation, grammar, word choice, tone, and structure. It then runs staged cleanup, AI editing, independent review (up to four rounds), and final verification, returning a clean text plus a change report, and also exposes rule search, rule packs, and diff auditing for use in Claude Desktop, Cursor, n8n, or custom code.

Enables AI agents to subscribe to a deterministic daily ethics feed drawn from a four-module curriculum and retrieve read-only tools, resources, and reflection prompts covering principles, case studies, and codes of conduct.

Enables AI assistants to look up any Pine Script function, search TradingView documentation, and find code examples in real time.

Provides controlled, verifiable access to jw.org content for AI applications, enabling search, article retrieval, and scripture lookup while preventing hallucinations.

Provides calculator tools for mathematical operations, access to TypeScript SDK documentation, and meeting summary prompt templates through both stdio and SSE transports.

Enables parsing and extraction of content from various document formats (PDF, Word, Excel, PowerPoint) into Markdown format using the Niutrans document API.

MCP server providing access to the Earth616 Defense Supply Chain and Documentation Ontology with tools for ontology lookup and testing.

Provides calculator tools for mathematical operations, document resources for accessing TypeScript SDK documentation, and prompt templates for generating structured meeting summaries. Built with FastMCP to demonstrate comprehensive MCP capabilities including tools, resources, and prompts in a single implementation.

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

A tool that helps users conduct comprehensive research on complex topics by exploring questions in depth, finding relevant sources, and generating structured, well-cited research reports.

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.

Enables reading, validating, and managing STEPWELL method documentation (BACKLOG.md, PROGRESS.md, archives) in repositories, with tools for status, backlog, progress, and safe dry-run mutations.

Provides semantic search capabilities over PyTorch documentation, enabling users to find relevant documentation, APIs, code examples, and error messages through Claude Code integration.

An MCP server that retrieves and cleans official documentation for popular AI/Python libraries via web search and LLM processing, exposing a get_docs tool.

Enables AI-powered document analysis and querying for project documentation using vector embeddings stored in Redis. Supports document upload, context-aware Q\&A, automatic test case generation, and requirements traceability through OpenAI integration.

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.