"Generating Metrics" matching MCP servers:

Builds interactive, filterable timelines from your own materials through an interview process, generating self-contained offline files with optional web publishing and cross-device sync.

Connects AI coding agents to Google NotebookLM, enabling querying notebooks, adding sources, and generating artifacts like reports, podcasts, and slide decks.

A design knowledge MCP server that Claude can query when generating UI, covering eight layers: principles, patterns, content design systems, research methods, service design, brand/visual, business strategy, and design tokens.

Enables AI music production planning by generating song specifications and project plans, converting them for Ableton, generating audio via Google Lyria, and managing memory, review, and orchestration workflows.

A simple notes system that allows creating, storing, and accessing text notes through MCP resources and tools, with prompt support for generating summaries of all stored notes.

Turns your data model into a semantic layer for AI agents, automatically generating typed, discoverable tools with entity relationships and schema discovery.

Enables agents to answer analytics questions with provable correctness by resolving metrics through the semantic layer, tracing lineage, traversing knowledge graphs, checking freshness, and searching glossary definitions, while enforcing access controls and logging every call.

Enables deep research evidence synthesis and citation verification by generating structured research graphs through the Model Context Protocol.

Graph-level operations for Karpathy LLM Wiki knowledge bases, enabling LLM agents via MCP and humans via CLI to perform structured graph surgery including node/edge CRUD, wikilink management, index rebuilding, and graph metrics.

Provides coding agents with durable, cross-session lessons-learned memory, enforcing that success or failure verdicts can only come from human approval, human correction, or objective metrics—never from the agent itself.

Provides tools for searching and reading Obsidian notes, creating flashcards, generating summaries, and planning study schedules via the MCP protocol.

An MCP server that turns your AI assistant into a structured ideation partner by generating, scoring, critiquing, and storing software ideas locally with a built-in web dashboard.

ktx is an MCP server that provides agents with accurate warehouse querying by serving a self-improving context layer of approved metrics, joinable columns, and business knowledge through CLI and MCP tools.

A Model Context Protocol server providing pre-curated canonical memory, prose/code provenance checking, and benchmark metrics to improve accuracy and reduce costs across AI tools.

Enables AI agents to read, write, and maintain documentation through an MCP server, providing tools for asking questions, generating docs, checking drift, and exploring knowledge graph.

Enables AI agents to locally search, query, and understand codebases with token-efficient context, dependency graphs, history, architecture diagrams, and metrics through MCP.

An MCP server that transforms codebases into knowledge graphs using Neo4J, enabling AI assistants to understand code structure, relationships, and metrics for more context-aware assistance.

Enables structured problem-solving through sequential thinking stages with persistent storage and analysis. Helps break down complex problems into manageable cognitive steps while tracking progress and generating summaries of the entire thought process.

MCP server for interacting with a Zotero library via the local API. Enables searching, retrieving, creating, updating, and deleting Zotero items, managing collections and tags, and generating citations.

Organizes personal research papers by crawling arXiv metadata, embedding abstracts, storing in a PostgreSQL database, and generating Related Work sections using LLMs.