VirtualFlyBrain

VFB3-MCP Server

A Model Context Protocol (MCP) server for Virtual Fly Brain (VFB): query Drosophila melanogaster neuroanatomy, connectomics, expression and reagent data from Claude, Copilot or any other MCP client.

Canonical repository: VirtualFlyBrain/VFB3-MCP (this one) — maintained by the Virtual Fly Brain team. Hosted service: https://vfb3-mcp.virtualflybrain.org — no installation, no API key, no account.

claude mcp add --transport http virtual-fly-brain https://vfb3-mcp.virtualflybrain.org

The earlier copy at Robbie1977/VFB3-MCP is archived at v1.3.0 and is not maintained; use this repository and the URL above.

This server provides tools to query VFB data, run queries, and search for terms. In HTTP mode it runs statelessly (no session tracking), so any replica can handle any request and standard load balancing works.

🚀 Quick Start

Use the Live Service (Recommended)

The easiest way to use VFB3-MCP is through our hosted service at https://vfb3-mcp.virtualflybrain.org. This requires no installation or setup on your machine.

Claude Desktop Setup

1. Open Claude Desktop and go to Settings
2. Navigate to the MCP section
3. Add a new MCP server with these settings:
   • Server Name: virtual-fly-brain (or any name you prefer)
   • Type: HTTP
   • Server URL: https://vfb3-mcp.virtualflybrain.org

Configuration JSON (alternative method):

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

Claude Code Setup

1. Locate your Claude configuration file:

   • macOS/Linux: ~/.claude.json
   • Windows: %USERPROFILE%\.claude.json

2. Add the VFB3-MCP server to your configuration:

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

3. Restart Claude Code for changes to take effect

GitHub Copilot Setup

1. Open VS Code with GitHub Copilot installed
2. Open Settings (Ctrl/Cmd + ,)
3. Search for "MCP" in the settings search
4. Find the MCP Servers setting
5. Add the server URL: https://vfb3-mcp.virtualflybrain.org
6. Give it a name like "Virtual Fly Brain"

Alternative JSON configuration (in mcp.json):

{  "servers": {    "virtual-fly-brain": {      "type": "http",      "url": "https://vfb3-mcp.virtualflybrain.org"    }  }}

Visual Studio Code (with MCP Extension)

1. Install the MCP extension for VS Code from the marketplace
2. Open the Command Palette (Ctrl/Cmd + Shift + P)
3. Type "MCP: Add server" and select it
4. Choose "HTTP" as the server type
5. Enter the server details:
   • Name: virtual-fly-brain
   • URL: https://vfb3-mcp.virtualflybrain.org
6. Save and restart VS Code if prompted

Other MCP Clients

For any MCP-compatible client that supports HTTP servers:

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

Gemini Setup

To use the Virtual Fly Brain (VFB) Model Context Protocol (MCP) server with Google Gemini, you can connect through custom Python/Node.js clients that support MCP.

Note: Direct Gemini web interface integration with MCP is not currently supported. Developer tools are needed to connect the two.

Option 1: 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.

Testing the Connection

Once configured, you can test that VFB3-MCP is working by asking your AI assistant questions like:

Basic Queries:

• "Get information about the neuron VFB_jrcv0i43"
• "Search for terms related to medulla in the fly brain"
• "What neurons are in the antennal lobe?"

Advanced Queries:

• "Find all neurons that connect to the mushroom body"
• "Show me expression patterns for gene repo"
• "What brain regions are involved in olfactory processing?"
• "Run a connectivity analysis for neuron VFB_00101567"

Search Examples:

• "Search for adult neurons in the visual system"
• "Find genes expressed in the central complex"
• "Show me all templates available in VFB"

If you see responses with VirtualFlyBrain data, including neuron names, brain regions, gene expressions, or connectivity information, the setup is successful!

For more detailed usage examples and API calls, see examples.md.

Example Workflow

1. Search for a term: "Search for neurons in the optic lobe"
2. Get detailed info: "Get information about VFB_00101567"
3. Run specific queries: "Show connectivity for VFB_00101567"
4. Explore relationships: "What neurons synapse in the mushroom body?"

🛠️ Available Tools

The MCP server exposes the following tools (available to assistants like Claude and Copilot):

• get_term_info — Get detailed metadata for a VFB ID
• run_query — Run a precomputed analysis query for a VFB ID (see the Queries field from get_term_info)
• search_terms — Search VFB entities by text with filtering / boosting options. This is the same search virtualflybrain.org itself runs
• list_search_facets — List the facets_annotation type names that search_terms' filter_types / exclude_types / boost_types / demote_types accept, optionally filtered by substring
• resolve_entity — Resolve an unresolved FlyBase-related query string (e.g., P{VT054895-GAL4.DBD} or a driver line / cell type label) to VFB/FlyBase IDs and metadata (not the same as VFB term search)
• resolve_combination — Resolve an unresolved split-GAL4 combination name or synonym into its component IDs
• list_connectome_datasets — List available connectome datasets (e.g., Hemibrain, FAFB)
• query_connectivity — Query connectivity across connectome datasets using upstream/downstream filters, returned as a strongest-first page plus a summary computed over every connection found
• get_predicted_neurotransmitters — Predicted neurotransmitter(s) for a neuron class (and its subclasses) from per-instance connectome predictions, per instance or aggregated with mean confidence, optionally split by dataset
• get_known_neurotransmitters — Known (curated) neurotransmitter(s) for a neuron class and its subclasses, from the ontology classification (no confidence)
• get_hierarchy — Traverse the ontology hierarchy for a VFB ID: part_of (region/tissue structure) and/or subclass_of (cell-type taxonomy), ancestors and/or descendants

🛠️ Local Installation

Prerequisites

• Node.js 18 or higher
• npm or yarn

Step-by-Step Installation

1. Clone the repository:

   bash

   git clone https://github.com/VirtualFlyBrain/VFB3-MCP.gitcd VFB3-MCP

2. Install dependencies:

   bash

   npm install

3. Build the project:

   bash

   npm run build

4. Start the server:

   bash

   npm start

Platform-Specific Setup

Claude Desktop (Local Development)

For local development with Claude Desktop, add this to your MCP configuration:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{  "mcpServers": {    "vfb3-mcp": {      "command": "node",      "args": ["/absolute/path/to/VFB3-MCP/dist/index.js"]    }  }}

Claude Code

Add to your claude.json file:

{  "mcpServers": {    "vfb3-mcp": {      "command": "node",      "args": ["/absolute/path/to/VFB3-MCP/dist/index.js"]    }  }}

Visual Studio Code

1. Install the MCP extension for VS Code
2. Press Cmd + Shift + P (macOS) or Ctrl + Shift + P (Windows/Linux)
3. Select MCP: Add server…
4. Choose Command type
5. Enter:
   • Name: vfb3-mcp
   • Command: node
   • Arguments: /absolute/path/to/VFB3-MCP/dist/index.js

GitHub Copilot

Configure the MCP server URL in your Copilot settings to point to your local server:

http://localhost:3000

For HTTP mode testing:

MCP_MODE=http PORT=3000 node dist/index.js

Docker Installation

Using Docker Compose (Recommended):

docker-compose up --build

Manual Docker Build:

# Build the imagedocker build -t vfb3-mcp .
# Run the containerdocker run -p 3000:3000 vfb3-mcp

Pull Pre-built Image:

docker pull virtualflybrain/vfb3-mcp:latestdocker run -p 3000:3000 virtualflybrain/vfb3-mcp:latest

Available Tools

get_term_info

Retrieve detailed information about VFB terms using their IDs.

Parameters:

• id (string): VFB ID (e.g., "VFB_jrcv0i43")
• force_refresh (boolean, optional): Bypass the response cache and recompute (see run_query below for the cost and the whitelisting caveat)

run_query

Execute predefined queries on VFB data.

Parameters:

• id (string or array): One or more VFB IDs (e.g., "VFB_00101567")
• query_type (string): Query type from the entity's Queries array (e.g., "PaintedDomains")
• queries (array, optional): {id, query_type} pairs for mixed batch queries
• limit (number, optional): Max rows per call (default 25). The true total is always returned as count; use 0 for all rows (still capped ~25000)
• offset (number, optional): Row offset for paging (default 0); re-run with offset += limit for the next page
• include_images (boolean, optional): Include the thumbnail column (default false — it is stripped to save space, and the response _note says how to re-add it)
• force_refresh (boolean, optional): Bypass the response cache and recompute. Expensive; intended only as a one-shot retry of a call that returned a stale or failed result. A failed query is already retried once automatically. Requires the server's egress IP to be whitelisted on v3-cached — from a non-whitelisted host nginx ignores the header and serves the cached entry unchanged.

The response reports count_status alongside count: exact (count is the true total), row_count (no total supplied — count is just the rows returned), or unavailable (the query failed upstream and count is -1). A -1 is an error indication, not an empty result set, and _note says so in words.

FlyBase stocks and split-GAL4 combination publications are run_query query_types too: FindStocks and FindComboPublications. FindStocks takes FlyBase feature, stock and strain (FBsn) IDs. For a split combination (FBco), FindStocks returns stocks with both hemidrivers where they exist, and otherwise falls back to stocks of each hemidriver; a match column says which.

search_terms

Search for VFB terms. This calls VFBquery's /search, which is the search virtualflybrain.org itself runs — the same Solr query, the same filters and boosts, the same final sort — so a result here is the result a user would see on the site. Deprecated terms are already excluded server-side; there is no need to ask for that.

Parameters:

• query (string): Search query (e.g., "medulla")
• filter_types (array, optional): Keep only results matching ALL of these facets_annotation types (AND logic)
• exclude_types (array, optional): Drop results matching ANY of these facets_annotation types (OR logic)
• boost_types (array, optional): Lift results matching these types up the ranking without excluding others
• demote_types (array, optional): Sink results matching these types to the bottom of the ranking without excluding them. Ignored for a type that also appears in boost_types
• unique (boolean, optional): One row per term (default true). Set false for one row per matching synonym, which shows which name matched at the cost of repeating IDs
• start (number, optional): Page start index (default 0)
• rows (number, optional): Rows to return (default 150, max 1000)
• minimize_results (boolean, optional): Return only the top 10 with reduced fields, for a first look (default false)
• auto_fetch_term_info (boolean, optional): When the query matches one term's name exactly, also fetch that term's info (default false)

Type names come from the live vocabulary — there are over 200 of them and they change as data is added, so call list_search_facets rather than guessing. The response reports returned (rows given), total (length of the ranked list), distinct_terms and solr_matches (terms Solr matched before ranking) separately, so a truncated page never looks like a small result set.

list_search_facets

List the valid facets_annotation type names for the four type filters above, read from the live vocabulary.

Parameters:

• contains (string, optional): Case- and separator-insensitive substring filter (e.g., "neuron", "nervous system")

If the deployed VFBquery predates the /facets endpoint, this falls back to a snapshot bundled with the server and says so — names absent from a snapshot result may still be valid.

query_connectivity

Query synaptic connectivity between neuron classes across all connectome datasets. At least one of upstream_type or downstream_type is required. Results are ranked strongest-first and paged: you get limit rows plus a summary computed over every connection found — weight min/max/total/mean, per-dataset counts, distinct neuron counts, and the top class pairs — so the totals stay true even though the rows are truncated. A broad query can find tens of thousands of connections, which is why paging is on by default.

Parameters:

• upstream_type / downstream_type (string, optional): Neuron class OWL ID or label. Anatomical regions are not accepted
• weight (number, optional): Minimum synapse count (recommended 5; use ≥50 when both ends are specified)
• group_by_class (boolean, optional): Aggregate to class pairs instead of neuron pairs — usually the better first call on a broad query
• exclude_dbs (array, optional): Dataset symbols to exclude (recommended ["hb","fafb"]); see list_connectome_datasets
• limit (number, optional): Rows to return, strongest first (default 50; 0 for all)
• offset (number, optional): Row to start from within the ranking (default 0)

get_predicted_neurotransmitters

Predicted neurotransmitter(s) for a neuron class — itself or any subclass — from per-instance connectome predictions (each reconstructed neuron carries a predicted transmitter with a confidence). Aggregated to flat per-class rows by default (one per cell type × neurotransmitter, with instances, percent_of_class, mean_confidence); aggregate=false gives one row per neuron. split_by_dataset=true adds a dataset column so agreement across connectomes is visible. The neurotransmitter is a GO secretion term (nt_id/nt_label) — the same id space as get_known_neurotransmitters. Distinct from get_known_neurotransmitters, which is the curated classification without confidence.

Parameters:

• neuron_type (string, required): Neuron class OWL ID or label (means the class and its subclasses)
• aggregate (boolean, optional): true (default) for per-class rows; false for per-instance rows
• split_by_dataset (boolean, optional): true (aggregate only) for one row per (cell type, neurotransmitter, dataset)
• exclude_dbs (array, optional): Dataset symbols to exclude (default ["hb","fafb"]; [] for all)
• min_confidence (number, optional): Drop predictions below this confidence (0..1)

get_known_neurotransmitters

Known (curated) neurotransmitter(s) for a neuron class and its subclasses, from the ontology's classification rather than per-instance predictions — so no confidence. One row per (cell type, neurotransmitter): {cell_type_id, cell_type, nt_id, nt_label}, with nt_id a GO secretion term. Empty when the ontology asserts none (try get_predicted_neurotransmitters for the data-driven answer).

Parameters:

• neuron_type (string, required): Neuron class OWL ID or label (means the class and its subclasses)

🧠 About VirtualFlyBrain

VirtualFlyBrain (VFB) is a comprehensive knowledge base about Drosophila melanogaster neurobiology, providing 3D images, gene expression data, neural connectivity information, and standardized terminology for fly brain research.

📖 Documentation

• LLM Guidance: Guide for AI assistants on using this MCP effectively
• Examples: Usage examples and integration guides
• Technical Documentation: Infrastructure, deployment, and development details

📄 License

MIT