MCPサーバーのランキングにContextual AIリランカーを使用

• •ターゲットAPIの認証情報が必要な場合あり
• •OpenAI API Key

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        "content": "# Dynamic MCP Selection\n## PROBLEM\nThousands of MCP Servers exist and many are updated daily, making server selection difficult for LLMs.\n- Current approaches require manually downloading and configuring servers, limiting flexibility.\n- When multiple servers are pre-configured, LLMs get overwhelmed and confused about which server to use for specific tasks.\n\n### This template enables dynamic server selection from a live PulseMCP directory of 5000+ servers.\n\n## How it works\n- A user query goes to an LLM that decides whether to use MCP servers to fulfill a given query and provides reasoning for its decision.\n- Next, we fetch MCP Servers from Pulse MCP API and format them as documents for reranking\n- Now, we use Contextual AI's Reranker to score and rank all MCP Servers based on our query and instructions\n\n## How to set up\n- Sign up for a free trial of Contextual AI [here](https://app.contextual.ai/) to find CONTEXTUALAI_API_KEY.\n- Click on variables option in left panel and add a new environment variable CONTEXTUALAI_API_KEY.\n- For the baseline model, we have used GPT 4.1 mini, you can find your OpenAI API key[ here](https://platform.openai.com/api-keys)\n\n## How to customize the workflow\n- We use chat trigger to initate the workflow. Feel free to replace it with a webhook or other trigger as required.\n- We use OpenAI's GPT 4.1 mini as the baseline model and reranker prompt generator. You can swap out this section to use the LLM of your choice.\n- We fetch 5000 MCP Servers from the PulseMCP directory as a baseline number, feel free to adjust this parameter as required.\n- We are using Contextual AI's ctxl-rerank-v2-instruct-multilingual reranker model, which can be swapped with any one of the following rerankers: \n   1) ctxl-rerank-v2-instruct-multilingual\n   2) ctxl-rerank-v2-instruct-multilingual-mini\n   3)  ctxl-rerank-v1-instruct\n- You can checkout this [blog](https://contextual.ai/blog/introducing-instruction-following-reranker/) for more information about rerankers to make informed choice.\n- If you have feedback or need support, please email reranker-feedback@contextual.ai"
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        "content": "## 1. Determine whether MCP servers are needed\nBased on user's request, LLM determines the need for an MCP Server, provides a reason, and if needed, provides reranking instruction text which will be passed to reranker"
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        "content": "## 2. Fetch MCP Server list and format them\nWe fetch 5000 MCP Servers from PulseMCP directory and parse them as documents to pass it onto the Contextual AI Reranker"
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        "content": "## 3. Rerank the servers and display top five results\nWe use Contextual AI's reranker to re-rank the servers and identify the top 5 servers based ont eh user query and re-ranker instruction, which is then formatted to be displayed in user friendly format.\n- You can checkout this [blog](https://contextual.ai/blog/introducing-instruction-following-reranker/) to learn more about rerankers"
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