"Figma Context MCP - Searching for Related Information or Tools" matching MCP servers:

A local-first MCP server that connects AI clients to Figma via a WebSocket bridge and native plugin, enabling deterministic design operations like inspection, creation, mutation, styling, validation, and optimization.

Fast, quota-free Figma automation for coding agents, enabling AI agents to read, edit, migrate, audit, screenshot, and prototype inside Figma Desktop files.

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.

Provides AI assistants with real-time visibility into your codebase's internal libraries, team patterns, naming conventions, and usage frequencies to generate code that matches your team's actual practices.

A reusable MCP server providing shared, versioned context across AI agents and devices via a private GitHub workspace, with tools to discover projects, bootstrap, query, and close out task state.

Persistent project context (AGENTS.md), cross-session memory, and identity — discoverable by any MCP client.

An adaptation of the MCP Sequential Thinking Server designed to guide tool usage in problem-solving. This server helps break down complex problems into manageable steps and provides recommendations for which MCP tools would be most effective at each stage.

An MCP server that enables processing of massive datasets up to 10M+ tokens using a recursive language model pattern for strategic chunking and analysis. It automates sub-queries and result aggregation using free local inference via Ollama or the Claude API to handle context beyond standard prompt limits.

Long AI conversations fail in predictable ways. Context-First fixes all four: Failure Mode What Goes Wrong Context-First Solution Context Drift AI forgets earlier decisions and intent as the conversation grows context_loop + detect_drift continuously re-anchor every turn Silent Contradiction New inputs silently overrule established facts — the AI doesn't notice detect_conflicts compares every inp

Provides persistent tool context that survives across Claude Desktop chat sessions, automatically injecting tool-specific rules, syntax preferences, and best practices. Eliminates the need to re-establish context in each new conversation.

A context preservation engine for AI agents that bundles project state into a markdown package and syncs it to clipboard or cloud, enabling zero knowledge loss across sessions.

Provides intelligent context management for AI development sessions, allowing users to track token usage, manage conversation context, and seamlessly restore context when reaching token limits.

An MCP server integration that enables Cursor AI to communicate with Figma, allowing users to read designs and modify them programmatically through natural language commands.

Provides local, explicitly scoped memory for coding agents via MCP, storing durable project knowledge in a per-repository SQLite database with tools to record, search, and retrieve context.

Enables users to control the cursor in Figma through verbal commands using an agentic AI agent, streamlining the design process with a new interaction method.

Enables multiple AI coding agents and humans to coordinate through a Git-native Markdown blackboard (.context/ + AGENTS.md), where they can inspect workspace overviews, claim and toggle ready tasks, record architecture decisions, synthesize shared prompts, and hand off work via atomic, lock-protected writes that prevent lost updates and formatting drift. It also serves a fully offline local web dashboard that live-syncs any blackboard change over WebSocket.

Provides computational tools for systematic reasoning, including boolean evaluation, date arithmetic, object counting, state tracking, and format validation, to enhance agent capabilities and eliminate calculation errors in reasoning tasks.

A dynamic MCP server that automatically discovers Python files in a directory and exposes them as tools to any MCP-compatible AI client, allowing users to easily create and deploy custom AI tools.

Enables LLM agents to compress handoffs into structured, auditable context capsules, preserving goals, constraints, decisions, and risks without external API calls.

Enables Claude Desktop and other AI tools to interact directly with Figma, allowing for powerful AI-assisted design capabilities through natural language commands.