Tools

• Allow your agents to interact with the outside world
• Can retrieve information from the web or private data stores
  • web search using public APIs
  • memorization and recall
  • check the weather forecast
  • semantic search a vector store
• Some can execute actions, e.g.
  • Send messages over a chat service
  • running software on your computer
  • approving/banning content in a moderation API
  • ordering parts and supplies
• Anything that can be done by software can be turned into a tool
• Recently tools standardized around Model Context Protocol (MCP)

Providers

• Tools are supplied by tool providers
• MCP providers can run locally or remotely
• Local providers can perform actions on your computer
• External providers can integrate with various web services

Configuration

• Add various providers in the Tools tab
• Saved to configuration file
• Remote providers may require authorization
  • Can specify an environment variable containing auth token
• Can specify timeout

Examples

MCP example (server-everything)

• A bit useless aside from verifying setup works
• Create a new STDIO tool
  • command: nix-shell
  • Arguments¹:

    -p
    nodejs_22
    --run
    npx -y @modelcontextprotocol/server-everything
    

• Peruse the tool schemas
• Try using Parse JSON and Invoke Tool to call these manually
• You can use an agent with Structured Output to help with the arguments

Tavily

• Web search API geared optimized for agents
• 1000 free requests / month
• Can run MCP server locally or use hosted
• First sign up and get an API key from Tavily
• Define an environment variable for the API key
  • TAVILY_API_KEY=***
• Start the app and define a new HTTP tool
  • URI: https://mcp.tavily.com/mcp/?tavilyApiKey={{api_key}}
  • Auth Var: TAVILY_API_KEY
• App substitutes {{api_key}} when issuing requests

EmbCP

• An example MCP server on top of Qdrant included in this repo
• You’ll need to index something yourself to get this running
• first run a qdrant server and create a collection
  • Included helper script: nix-shell . --run qdrant-serve
• Create collection and import some example points using emberlain

  cd emberlain
  cargo run -- \
    --progress \
    --llm-model my-qwen3-coder:30b \
    --embed-model MxbaiEmbedLargeV1Q \
    --collection goose \
    /code/upstream/goose/crates/goose/src/agents/
  

  • You’ll need to select a suitable provider and model for your setup
  • Select an embedding model that performs best for your case
  • Also supply a code path that exists on your system
  • This can take a while and/or burn through credits, depending on your provider
  • Import is incremental
    • you can interrupt and resume
    • or index parts of the repo selectively
• Install embcp: nix-env --install -f . -A embcp-server.bin
• Define a new stdio tool
  • Command: embcp-server
  • Arguments¹:

    --embed-model
    MxbaiEmbedLargeV1Q
    --collection
    goose
    

    • Must use same embedding model and collection as previous step
    • Qdrant serve must still be running independently

In workflows

• In workflows, can restrict which tools a given agent can use
• Can also invoke tools manually for more control over inputs and results
• Tools will automatically be used by the Chat Node if LLM opts to use
• In Structured Chat, a tool must be used
• Tool invocation can fail for numerous reasons
  • Can use Fallback node to recover
• External tools may fail to respond in a reasonable time
  • Set the timeout in the provider configuration

1. Placement of lines matters here. Each argument to the defined Command should be on a separate line. Multiple words on a single line will be treated as a single argument. ↩ ↩2