PROBLEM
Thousands of MCP Servers exist and many are updated daily, making server selection difficult for LLMs.
- Current approaches require manually downloading and configuring servers, limiting flexibility.
- When multiple servers are pre-configured, LLMs get overwhelmed and confused about which server to use for specific tasks.
This template enables dynamic server selection from a live PulseMCP directory of 5000+ servers.
How it works
- 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.
- Next, we fetch MCP Servers from Pulse MCP API and format them as documents for reranking
- Now, we use Contextual AI's Reranker to score and rank all MCP Servers based on our query and instructions
How to set up
- Sign up for a free trial of Contextual AI here to find CONTEXTUALAI_API_KEY.
- Click on variables option in left panel and add a new environment variable CONTEXTUALAI_API_KEY.
- For the baseline model, we have used GPT 4.1 mini, you can find your OpenAI API key here
How to customize the workflow
- We use chat trigger to initate the workflow. Feel free to replace it with a webhook or other trigger as required.
- 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.
- We fetch 5000 MCP Servers from the PulseMCP directory as a baseline number, feel free to adjust this parameter as required.
- We are using Contextual AI's ctxl-rerank-v2-instruct-multilingual reranker model, which can be swapped with any one of the following rerankers:
- ctxl-rerank-v2-instruct-multilingual
- ctxl-rerank-v2-instruct-multilingual-mini
- ctxl-rerank-v1-instruct
- You can checkout this blog for more information about rerankers to learn more about them.
Good to know:
- Contextual AI Reranker (with full MCP docs): ~$0.035/query
Includes 0.035 for reranking + ~$0.0001 for OpenAI instruction generation.
- OpenAI Baseline: ~$0.017/query
