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VFB3-MCP Examples

This file contains examples of how to use the VFB3-MCP server tools.

Tool Examples

1. Get Term Info

{
  "method": "tools/call",
  "params": {
    "name": "get_term_info",
    "arguments": {
      "id": "VFB_jrcv0i43"
    }
  }
}

2. Run Query

{
  "method": "tools/call",
  "params": {
    "name": "run_query",
    "arguments": {
      "id": "VFB_00101567",
      "query_type": "PaintedDomains"
    }
  }
}

3. Search Terms

{
  "method": "tools/call",
  "params": {
    "name": "search_terms",
    "arguments": {
      "query": "medulla"
    }
  }
}

4. Search Terms with Filtering

{
  "method": "tools/call",
  "params": {
    "name": "search_terms",
    "arguments": {
      "query": "medulla",
      "filter_types": ["neuron", "adult"],
      "boost_types": ["has_image"]
    }
  }
}

Deprecated terms are already excluded server-side, so there is no need to list them in exclude_types. Get valid type names from list_search_facets rather than guessing.

4b. List Valid Facet Type Names

{
  "method": "tools/call",
  "params": {
    "name": "list_search_facets",
    "arguments": {
      "contains": "neuron"
    }
  }
}

4c. One Row Per Matching Synonym

{
  "method": "tools/call",
  "params": {
    "name": "search_terms",
    "arguments": {
      "query": "Kenyon cell",
      "unique": false
    }
  }
}

Useful when you need to know which name matched. The same short_form will repeat across rows; the default unique: true gives one row per term instead.

5. Search Terms with Minimization

{
  "method": "tools/call",
  "params": {
    "name": "search_terms",
    "arguments": {
      "query": "medulla",
      "minimize_results": true,
      "rows": 20
    }
  }
}

6. Search Terms with Auto Term Info

{
  "method": "tools/call",
  "params": {
    "name": "search_terms",
    "arguments": {
      "query": "antennal lobe",
      "auto_fetch_term_info": true
    }
  }
}

7. Resolve an Entity Name

{
  "method": "tools/call",
  "params": {
    "name": "resolve_entity",
    "arguments": {
      "name": "P{VT054895-GAL4.DBD}"
    }
  }
}

8. Find Stocks for a Feature ID

{
  "method": "tools/call",
  "params": {
    "name": "run_query",
    "arguments": {
      "id": "FBst123456",
      "query_type": "FindStocks"
    }
  }
}

For a split-GAL4 combination, pass its FBco (e.g. FBco0000061, MB109B). Each row then has a match value (Exact combination, Hemidriver alone or Hemidriver in other combination), and only the best available tier is returned.

9. Resolve a Split-GAL4 Combination

{
  "method": "tools/call",
  "params": {
    "name": "resolve_combination",
    "arguments": {
      "name": "SS04495" 
    }
  }
}

10. Find Publications for a Split-GAL4 Combination

{
  "method": "tools/call",
  "params": {
    "name": "run_query",
    "arguments": {
      "id": "FBco_0001234",
      "query_type": "FindComboPublications"
    }
  }
}

11. List Connectome Datasets

{
  "method": "tools/call",
  "params": {
    "name": "list_connectome_datasets",
    "arguments": {}
  }
}

12. Query Connectivity

{
  "method": "tools/call",
  "params": {
    "name": "query_connectivity",
    "arguments": {
      "upstream_type": "FBbt_00003789",
      "downstream_type": "FBbt_00003730",
      "weight": 5,
      "group_by_class": true,
      "exclude_dbs": ["mc"]
    }
  }
}

Tm1 → T3 across every connectome dataset except male-CNS: 11,916 connections unfiltered, 7,309 with mc excluded. At least one of upstream_type / downstream_type is required.

exclude_dbs takes dataset symbols, and an unrecognised symbol is silently ignored rather than reported — exclude_dbs: ["male-cns"] or ["hemibrain"] excludes nothing and says nothing. Call list_connectome_datasets and use the symbol field: BANC, fw, ol, mv, hb, mc, fafb, l1em.

Results come back as a strongest-first page plus a summary computed over every connection found, so count is the true total and returned is only what you were given. Broad queries find tens of thousands of connections.

12b. Paging Through Connectivity Results

{
  "method": "tools/call",
  "params": {
    "name": "query_connectivity",
    "arguments": {
      "upstream_type": "FBbt_00003789",
      "weight": 50,
      "limit": 25,
      "offset": 25
    }
  }
}

Rows 25–50 of the strongest-first ranking. The summary is identical on every page because it always covers the full result set — answer from it rather than from the rows.

12c. Neurotransmitters (predicted and known)

{
  "method": "tools/call",
  "params": {
    "name": "get_predicted_neurotransmitters",
    "arguments": {
      "neuron_type": "Tm9",
      "split_by_dataset": true
    }
  }
}

Predicted neurotransmitter(s) for Tm9 (and its subtypes), from per-instance connectome predictions. Aggregated per class by default — one row per cell type × neurotransmitter, with instances, percent_of_class and mean_confidence; split_by_dataset: true gives one row per (cell type, neurotransmitter, dataset). aggregate: false returns one row per neuron. The neurotransmitter is a GO secretion term (nt_id, e.g. GO_0014055 acetylcholine). Tm9 comes back ~100% acetylcholine.

{
  "method": "tools/call",
  "params": {
    "name": "get_known_neurotransmitters",
    "arguments": { "neuron_type": "Tm9" }
  }
}

The curated neurotransmitter(s) for Tm9 and its subclasses, from the ontology classification (no confidence) — one row per (cell type, GO term). Use get_predicted_neurotransmitters for the data-driven prediction and its confidence, and get_known_neurotransmitters for the established classification; they share the GO-term id space, and known-NT is empty when the ontology asserts none.

13. Get Ontology Hierarchy

Traverse part_of (region structure) or subclass_of (cell-type taxonomy) for a VFB term. relationship is required; direction defaults to both and max_depth to 1.

{
  "name": "get_hierarchy",
  "arguments": {
    "id": "FBbt_00005801",
    "relationship": "part_of",
    "direction": "descendants",
    "max_depth": 1
  }
}

Returns the direct parts of the mushroom body (FBbt_00005801). Use relationship: "subclass_of" for cell-type taxonomies (e.g. FBbt_00003686, Kenyon cell), and increase max_depth to expand deeper.

Integration with MCP Clients

Claude Desktop

Add to your MCP server configuration:

{
  "mcpServers": {
    "vfb3-mcp": {
      "type": "http",
      "url": "https://vfb3-mcp.virtualflybrain.org",
      "tools": ["*"]
    }
  }
}

Claude Code

Add to your claude.json:

{
  "mcpServers": {
    "vfb3-mcp": {
      "type": "http",
      "url": "https://vfb3-mcp.virtualflybrain.org",
      "tools": ["*"]
    }
  }
}

GitHub Copilot

Configure the MCP server URL in your Copilot settings pointing to https://vfb3-mcp.virtualflybrain.org.

Gemini

To use the Virtual Fly Brain (VFB) Model Context Protocol (MCP) server with AI on Google Search, connect through the Gemini CLI or a custom Python/Node.js client.

The Gemini web interface does not directly support custom MCP integration. Developer tools are needed to connect the two.

Option 1: Using Gemini CLI

The Gemini CLI allows direct registration of remote MCP servers.

Install the CLI: Ensure Node.js is installed, then run:

npm install -g @google/gemini-cli

Add the VFB Server: Use the add command with the VFB URL.

gemini mcp add vfb https://vfb3-mcp.virtualflybrain.org

Verify & Use: Run the CLI by typing gemini. Check the connection with /mcp. AI on Google Search will then call VFB's neuroanatomy and connectivity tools when questions about Drosophila are asked.

Option 2: Using Python

For application development, use the mcp and google-genai libraries to connect.

Setup: pip install google-genai mcp

Implementation: Use a streamable HTTP transport in JSON response mode (e.g. enableJsonResponse: true) to connect to the VFB URL, list its tools, and pass their schemas to the Gemini model as Function Declarations.

Docker Usage

# Build and run
docker-compose up --build

# Or manually
docker build -t vfb3-mcp .
docker run -it vfb3-mcp