"Information about CAD (Computer-Aided Design)" matching MCP servers:

Enables coding agents to turn mechanical requirements into validated FreeCAD models with deterministic sizing from published standards, standard-part provenance, and automatic geometry validation.

MCP server bridging ChatGPT-compatible clients to the Design Flow System, enabling project session management, draft import, round locking, and read-only access to design state and traceability.

Provides source-backed design context, route card validation, contract generation, critique and verification reports, evidence packages, Penpot change plans, and anti-repeat checks for design workflows. Does not directly mutate Penpot, but consumes read-only Penpot snapshots.

Provides seamless integration with Capacities knowledge management system, allowing Claude to list spaces, retrieve space information, search content, save weblinks, and add content to daily notes.

Design contract layer for AI agents. Scans Figma, code, Storybook, and token files, reconciles conflicts, and serves a single machine-readable source of truth so every agent gets the same authoritative design rules before it builds. Local-first.

Persistent decision memory and contradiction detection for AI coding agents. Enforces architectural consistency across sessions — the agent cannot code until it loads prior decisions. Human resolves conflicts on a dashboard or in chat.

A daily-rhythm support MCP server for ADHD and bipolar disorder, providing 23 tools for mood tracking, social rhythm regularity, early warning detection, task breakdown, and crisis support, all running locally with zero dependencies.

Enables AI agents to search a local SQLite knowledge base before fetching, save fetched documents with deduplication, and retrieve full text with metadata, preventing redundant fetches and fabrication.

Enables Alexa+ and other MCP clients to store memories as files and recall them via spreading activation over an association graph shaped by Hebbian learning, the spacing effect, and salience. Sleep consolidation plus graded compression tiers shrink faded memories into schemas and residuals without ever deleting them or dropping below measured fidelity floors, and the server alerts the user instead of pruning when the storage budget runs out.

An improved implementation of persistent memory using a local knowledge graph with a customizable --memory-path. This lets Claude remember information about the user across chats.

AutoMem MCP provides persistent memory for AI assistants, enabling them to recall information across conversations and platforms with graph-vector retrieval.

Public, agent-searchable knowledge base about AI-native software development, served as an MCP server with tools to search, read, and list topics.

Exposes tools to ingest FigJam boards, retrieve context summaries, and answer free-form questions about the board content.

A local MCP server that maintains a persona profile (contact library) for AI assistants, allowing them to create, search, retrieve, supplement, and correct information about people mentioned in conversations via tools like search_profiles, get_profile, create_profile, update_profile, add_facts, update_fact, and delete_fact.

A personal knowledge hub MCP server that enables LLMs to query structured information about Akib Dabgar's skills, experience, projects, and goals using Dynamic Context Retrieval for efficient context usage.

Enables MCP-compatible agents to answer questions about you by exposing your CV through a tool, resource, and prompt.

MCP server for the Graphite Financial Knowledge Graph, enabling natural language queries about companies, supply chains, executives, regulations, and patents via MCP-compatible clients.

Gives AI coding agents persistent, evolving knowledge about a codebase, enabling them to store and retrieve observations about architecture, conventions, gotchas, and recent work context.

Provides a compressed knowledge graph of the NVIDIA AI developer stack for deterministic traversal, enabling agents to answer questions about dependencies and prerequisites with minimal tokens.

Local-first, code-aware engineering cognition for AI coding agents - persistent memory, contextual retrieval, and continuous cognition about your codebase.