"Tools or methods for generating graphs from structured data" matching MCP servers:

MCP server providing managed persistent memory for AI agents. Read and write structured state across sessions, tools, and restarts at 1000+ requests per second, with no infrastructure to self-host or operate.

Serves a central playbook hub that provides situational guidance to LLM agents, lets clients list available playbooks, fetch procedural guidance, and apply graph mutations for persistent self-evolution.

Enables AI agents to perform local video, audio, and file operations inside an isolated workspace, including cutting/concat videos, extracting audio, transcribing, and managing files, with typed responses and background job support.

An enhanced MCP server that enables users to interact with NotebookLM while ensuring strict source fidelity and citation accuracy through client-side prompt structuring. It guides Claude to transform user queries into structured prompts that prevent the addition of external knowledge during document analysis.

Enforces disciplined programming practices by requiring AI assistants to audit their work and produce verified outputs at each phase of development, following structured workflows for refactoring, feature development, and testing.

Make it easy for agents to build their context about your projects over time The server provides a set of tools to help agents accumulate knowledge about a project over time in a structured way.

A Windows-optimized server providing universal data analytics for JSON and CSV files through over 32 tools including schema discovery and interactive visualizations. It is specifically designed for seamless integration with Claude Desktop on Windows.

A personal MCP bridge that lets ChatGPT drive a local Codex CLI, translating instructions into Codex app-server threads and returning compact summaries of what Codex did.

A fully working MCP server built from scratch in plain Node.js, implementing tools, resources, prompts, notifications, and sampling according to the MCP specification, designed to connect to Claude Desktop or any MCP client.

An MCP server that exposes ISO 20022 postal-address classification, assessment, and remediation tools for AI agents, enabling them to prepare debtor/creditor addresses for the November 2026 structured-address cutover.

A local MCP server that enables AI applications like Claude Desktop to securely access and work with Obsidian vaults, providing capabilities for reading notes, executing templates, and performing semantic searches.

MCP server for personal knowledge management providing AI-powered tools for semantic search, audio transcription, image generation, and PPT creation.

Provides five safe tools (calc, search, model_drift, compare_runs, grade_answer) for arithmetic, keyword search, model drift monitoring, eval comparison, and answer grading against sources, all implemented directly from the MCP spec with zero dependencies.

A Model Context Protocol server that extracts embedded data (such as i18n translations or key/value configurations) from TypeScript/JavaScript source code into structured JSON configuration files.

An MCP server that provides a persistent sandbox for AI coding agents to explore codebases server-side, returning only compact summaries to reduce context consumption.

Enables extraction of recipes from PDFs or JPG files and conversion into structured HTML, with interactive region selection and OCR via a web GUI.

Enables validating JSON/JSONC, getting/setting values with jq-like paths, pretty-printing or minifying, validating against Ajv JSON Schema, and diffing local files or inline strings without network calls.

Exposes four CMS public datasets as callable tools in Claude conversations, enabling natural-language real-world evidence analytics without SQL expertise.

Enables coding agents to run Playwright e2e tests, validate Supabase RLS policies with role-based SQL, typecheck TypeScript, execute npm scripts with parsed test output, and validate Next.js builds, all through a structured MCP interface.

MCP server for compressing AI embeddings by 5-7x using TurboQuant (PolarQuant + QJL), with tools to compress, decompress, estimate savings, and embed+compress vectors.