Follow these steps to launch and test the MCP KagiSearch server and chatbot in a temporary Meshagent Room.

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1. Prepare Your Environment

  • Install the Meshagent CLI and dependencies: 
    pip install "meshagent[all]"
  • Sign up and authenticate

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2. Required Environment Variables

Provide these on the CLI using --env:
  • KAGI_SUMMARIZER_ENGINE — Set to cecil
  • KAGI_API_KEY — Your Kagi API Key (YOUR_API_KEY_HERE)
Example: 
--env KAGI_SUMMARIZER_ENGINE=cecil --env KAGI_API_KEY=YOUR_API_KEY_HERE

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3. Start MCP Server Service in a Room

meshagent service test --room=test --role=agent --image=meshagent/mcp-kagisearch:latest \
  --env MESHAGENT_PORT=8001 --env KAGI_SUMMARIZER_ENGINE=cecil --env KAGI_API_KEY=YOUR_API_KEY_HERE \
  --port="num=8001 path=/webhook liveness=/ type=meshagent.callable" \
  --name=mcp-kagisearch-service-test
  • This starts a MCP KagiSearch server as a Meshagent agent in the specified Room (test). If the room is closed or idle, the tool and agent are also automatically removed.

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4. Start the Chatbot With Access to KagiSearch

meshagent chatbot join --room=test --agent-name=mcp-kagisearch --name=mcp-kagisearch --toolkit=mcp-kagisearch
  • Starts a chatbot agent in the same Room connected to the MCP KagiSearch toolkit.
  • A link to the room will be displayed in the terminal output.

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5. Interact in the Room

Visit the room link in your browser and send a message to the agent to interact with the KagiSearch tools via the chatbot.

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Project Level Deployment

For production or always-on availability, you can deploy both the MCP KagiSearch server and chatbot as persistent project-level Meshagent services. These services will automatically join every newly created Room in your project.

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1. Create the MCP KagiSearch Service

meshagent service create --role=agent --image=meshagent/mcp-kagisearch:latest \
  --env MESHAGENT_PORT=8001 --env KAGI_SUMMARIZER_ENGINE=cecil --env KAGI_API_KEY=YOUR_API_KEY_HERE \
  --port="num=8001 path=/webhook liveness=/ type=meshagent.callable" \
  --name=mcp-kagisearch-service

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2. Create the Chatbot Service

meshagent service create --image="meshagent/cli:latest" \
  --port="num=9001 path=/agent liveness=/ type=meshagent.callable participant_name=mcp-kagisearch-chatbot" \
  --env="MESHAGENT_PORT=9001" \
  --name="mcp-kagisearch-chatbot-service" \
  --command="meshagent chatbot service --agent-name=mcp-kagisearch-chatbot --toolkit=mcp-kagisearch"
Now, every Room created in that project will automatically have access to the KagiSearch MCP tools and chatbot, with no additional setup.

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Tools Available

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Tools Provided by This Server

ToolShort Description
kagi_search_fetchFetch web results based on one or more queries using the Kagi Search API.
kagi_summarizerSummarize content from a URL using the Kagi Summarizer API.

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Tool Details

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kagi_search_fetch

Fetch web results based on one or more queries using the Kagi Search API. Use for general search and when the user explicitly tells you to ‘fetch’ results/information. Results are from all queries given. They are numbered continuously, so that a user may be able to refer to a result by a specific number.
ParameterTypeDescription
queriesarrayOne or more concise, keyword-focused search queries. Include essential context within each query for standalone use.

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kagi_summarizer

Summarize content from a URL using the Kagi Summarizer API. The Summarizer can summarize any document type (text webpage, video, audio, etc.).
ParameterTypeDescription
urlstringA URL to a document to summarize.
summary_typestring optionalType of summary to produce. Options are ‘summary’ for paragraph prose and ‘takeaway’ for a bulleted list of key points.
target_languagestring optionalDesired output language using language codes (e.g., ‘EN’ for English). If not specified, the document’s original language influences the output.