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cerase-search MCP

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cerase-search-mcp

An MCP server that answers a web-search query with a short, sourced answer. It does not crawl anything itself: it sends the query to a search-capable model through an OpenAI-compatible LiteLLM proxy and returns the model's answer with the URLs behind its inline [n] citations.

Tools

Tool

What it does

Arguments

Returns

search

Everyday lookup of current information, through the model alias search.

agent_id, query

{answer, model, sources}

deepsearch

Multi-step research with broader source coverage, through the model alias deepsearch. Costs more than search.

agent_id, query

{answer, model, sources}

sources is a list of {index, url, title}, where index matches the [n] marker in answer. It is read from the model response in one of three shapes (message.annotations[].url_citation, a top-level citations list, or a top-level search_results list) and is empty when the model returned none; the server never invents a source.

agent_id must not be empty. Inside Cerase the gateway fills it with the calling assistant's id, and the server forwards it to LiteLLM as request metadata (metadata.cerase_agent_id) so the spend is attributed to that assistant.

Related MCP server: @semidark/mcp-litellm-searxng

Settings

Variable

Default

Purpose

LITELLM_BASE_URL

http://cerase-litellm:4000

Base URL of the OpenAI-compatible proxy; requests go to <base>/v1/chat/completions. The default is the LiteLLM service name inside a Cerase appliance.

LITELLM_MASTER_KEY

empty

API key sent to the proxy.

CERASE_SEARCH_ALIAS

search

Model name search calls.

CERASE_DEEPSEARCH_ALIAS

deepsearch

Model name deepsearch calls.

Installation

The connector is published in the Cerase Marketplace as studio.guidance/cerase-search (marketplace page). Every Cerase appliance installs it at boot, so its assistants have it without an install step.

The image ghcr.io/cerase-ai/cerase-search-mcp is built and published from the copy of these files kept in the Cerase appliance repository, which is private. This repository carries the same files byte for byte, so a change made only here does not reach the image.

Build and run locally

docker build -t cerase-search-mcp .
docker run --rm -p 3000:3000 \
  -e LITELLM_BASE_URL=https://<your-openai-compatible-proxy> \
  -e LITELLM_MASTER_KEY=<your-key> \
  cerase-search-mcp

server.py speaks MCP over stdio; the image runs it behind mcp-proxy, which serves Streamable HTTP at http://localhost:3000/mcp and SSE at http://localhost:3000/sse. The proxy must serve models named search and deepsearch (or the names set in the two alias variables), and they must be models that search the web, since the server sends a plain chat completion.

The image's HEALTHCHECK runs scripts/healthcheck.py, an MCP client that completes the handshake and lists the tools over /mcp. Its CERASE_HEALTHCHECK_* variables exist to point it at a stub in tests.

License

MIT. See LICENSE.

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