"Finding people on LinkedIn by their name" matching MCP servers:

Enables AI agents to publish posts, images, comments, and reactions to LinkedIn as the authenticated user, with built-in safety features like daily budgets and deduplication.

Enables an AI agent to fully operate a LinkedIn account—posting, messaging, profile edits, and network management—with self-healing selectors, human-like pacing, and approval-based planning to avoid breakage or lockouts.

On Board is a local MCP server that gives multiple AI agents and IDEs a shared project memory, ticket queue, and handoff history, so agents can seamlessly continue each other's work. It supports agent-to-agent wake events, enabling autonomous workflows like reject-fix-resubmit cycles without human relay.

An MCP server providing AI-powered cybersecurity, penetration testing, reconnaissance, vulnerability assessment, and security automation tools through the Model Context Protocol.

A simple Nuxt application that serves as a Model Context Protocol server deployable on Vercel, allowing developers to implement AI tools, prompts, and resources through the MCP TypeScript SDK.

A Model Context Protocol server implementation that runs on Cloudflare Workers with OAuth login, allowing Claude and other AI models to access tools via the MCP standard.

A serverless implementation of Model Context Protocol on Cloudflare Workers that enables connecting AI assistants like Claude to custom tools without authentication.

fomox402 broker + MCP server for last-bidder-wins games on Solana. Drop one URL into Claude / Cursor / Goose / Cline and the agent auto-registers a Privy-managed Solana wallet (faucet-funded with ~0.0024 SOL + $fomox402), bids via x402 micropayments, claims dividends, and plays autonomously. 14 tools wired up — register_agent, place_bid, claim_winnings, claim_dividend, list_games, get_me, etc. Str

AI-driven command center for local crypto operations, integrating over a dozen MCPs for real-time market data, technical analysis, on-chain data, and autonomous trading agent execution.

This server enables remote job discovery, tailored CV creation, and approval-gated application lifecycle management. It prioritizes safe, factual job applications without authenticated LinkedIn scraping or silent submissions.

Fundraising infrastructure for AI agents on Solana: register agents, create milestone-escrowed campaigns, and donate via the x402 pay-to-call flow. Backed by Anchor programs (agent_registry, escrow, reputation) with on-chain reputation for every verified action.

Lets agents query their own usage ledger captured by the Agentic Ledger proxy: sessions with full call traces, costs, loop runs, and stuck-loop flags. Tools include list_sessions, get_session, search, list_runs, get_run_status, and explain.

Enables AI agents to participate in Solana mainnet contests judged by AI, with tools for registration, entry submission, and payout tracking, earning real USDC on-chain.

Your AI finds the right people for you. Agent-to-agent networking via MCP. Publish what you need, match against other agents, both humans approve before connecting. Ed25519 signed, hosted API.

Provides persistent identity and memory for AI agents across MCP-compatible harnesses, enabling agents to retain their name, values, and episodic memories between sessions regardless of the client or model.

Enables Claude agents to checkpoint their context state and reset back to saved points with handoff messages, maintaining clean context windows during complex tasks by avoiding fragmented summaries from compactions.

Reduces Claude's context window costs by automatically summarizing inactive files to their public interfaces using AST parsing, keeping only the full contents of the currently active file.

Official implementation of Anthropic's 'think' tool that provides Claude with a dedicated space for structured reasoning, improving performance by up to 54% on complex tasks requiring multi-step problem solving.

Integrates Agent Skills into LLMs by discovering skills in a configured directory and progressively loading their instructions and resources on demand through a structured disclosure pattern.

Enables AI agents to learn from their sessions by inspecting history, proposing rules or skills, and automatically persisting them to configuration files.