youtube_mcp

Persistent Adaptive Planning Intelligence - structured loop engineering for AI coding assistants, with memory that persists across sessions, tools, and teammates.

A Model Context Protocol (MCP) server that integrates with Jina AI Search Foundation APIs.

5 AI skills as MCP tools: MBTI relationship sandbox (2-20 people), fact-checking (3 methods: source-tracing, cross-reference, logic-check), deep business analysis (5-layer), game audio brief generator, weekly skill radar. All monetized via Alipay AI Pay (¥0.10-0.50/call).

A Model Context Protocol server that provides access to Redis databases. This server enables LLMs to interact with Redis key-value stores through a set of standardized tools.

Web content fetching and conversion for efficient LLM usage

A Model Context Protocol server that provides time and timezone conversion capabilities. This server enables LLMs to get current time information and perform timezone conversions using IANA timezone names, with automatic system timezone detection.

Channel management and messaging capabilities

Query verified 2026 US dental cost data from Real Dental Costs: average implant, veneer, braces and procedure prices for all 50 states and 206 cities, state-vs-state comparisons, and the composite US Dental Cost Index. Open data (CC BY 4.0, Zenodo DOIs). Every response includes the source and a reference URL. Pricing/market research, not medical advice. Data: https://realdentalcosts.com

MCP Advisor & Installation - Use the right MCP server for your needs

VC follow intelligence for AI agents. Track what top investors follow on X — detect new follows, convergence signals, and trending companies before they're announced.

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An MCP server implementation for retrieving information from the AWS Knowledge Base using the Bedrock Agent Runtime.

ContextBridge (CB) is a local-first retrieval layer that sits between your codebase and your AI coding agent. Instead of pasting raw source files into context or letting the AI guess which files matter, CB indexes your codebase's real structure (via Graphify) and returns a compact, ranked result — owner file, related files, key symbols, and dependency chains — grounded in your actual code. A typical response is ~4–8 KB instead of the 100+ KB of raw source an AI would otherwise need to read to answer the same question — roughly a 96% cut in input tokens sent to your cloud AI, with zero hallucinated file paths or method names.

A Model Context Protocol Server connector for Perplexity API, to enable web search without leaving the MCP ecosystem.