MCP server

VirtualFlyBrain Official

org.virtualflybrain/vfb3-mcp

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

MCP server for Drosophila neuroscience data from VirtualFlyBrain

Website Source

At a glance

Type

MCP server

Publisher

virtualflybrain.org (verified by the MCP Registry)

Status

Working at last check

Tools

11

Token cost

About 6,496 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

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

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.vfb3-mcp]
url = "https://vfb3-mcp.virtualflybrain.org"

Gemini CLI

{"mcpServers":{"vfb3-mcp":{"httpUrl":"https://vfb3-mcp.virtualflybrain.org"}}}

Cursor

{"mcpServers":{"vfb3-mcp":{"url":"https://vfb3-mcp.virtualflybrain.org"}}}

VS Code

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

Antigravity

agy mcp add vfb3-mcp https://vfb3-mcp.virtualflybrain.org

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"vfb3-mcp","server_url":"https://vfb3-mcp.virtualflybrain.org","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"vfb3-mcp","server_url":"https://vfb3-mcp.virtualflybrain.org"}

Tools

• get_hierarchy ~386 tokens
  Build a hierarchy tree for a VFB term, showing ancestors (parents) and/or descendants (children). Use relationship "part_of" for brain region structure (e.g. "what are the parts of the mushroom body?") and "subclass_of" for cell type hierarchies (e.g. "what types of Kenyon cell are there?"). Descend
• get_known_neurotransmitters ~300 tokens
  Get the KNOWN (curated) neurotransmitter(s) for a Drosophila neuron class and its subclasses, from the ontology's classification rather than per-instance predictions — so there is no confidence. Use this for "what neurotransmitter is <cell type> known to use?" when you want the curated/established a
• get_predicted_neurotransmitters ~679 tokens
  Get the PREDICTED neurotransmitter(s) for a Drosophila neuron class — itself or any subclass — from per-instance connectome predictions (each reconstructed neuron carries a predicted transmitter with a confidence). Use this for "what neurotransmitter does <cell type> use?" when you want the data-dri
• get_term_info ~536 tokens
  Get term info for a VFB or anatomy ontology entity (VFB_*, FBbt_*, etc.). THIS IS THE QUERY DISCOVERY TOOL: the response's "Queries" array lists the valid query_type values that run_query accepts for this entity. ALWAYS call get_term_info before run_query unless you already obtained the query_type f
• list_connectome_datasets ~110 tokens
  List available connectome datasets with their labels and symbols. Use the returned symbols when constructing exclude_dbs arguments for query_connectivity. Common datasets include Hemibrain (hb), FAFB (fafb), MANC, and others. Call this tool if unsure which dataset symbols are valid.
• list_search_facets ~230 tokens
  List the type names search_terms can filter, exclude, boost or demote by, with the number of terms carrying each one. Call this instead of guessing: there are over 200 names, they are the index's own annotations rather than a curated list, and they change as data is added. Use contains to narrow (e.
• query_connectivity ~1294 tokens
  Query synaptic connectivity between Drosophila neuron classes across ALL connectome datasets simultaneously for comparative connectomics. This is NOT pre-cached — it runs live queries, so expect slow responses (up to several minutes). Set both upstream_type AND downstream_type to filter connections
• resolve_combination ~294 tokens
  Resolve an unresolved split-GAL4 combination name or synonym into its FBco ID and component hemidrivers. Pass the raw combination text exactly as the user wrote it (for example "MB002B" or "SS04495"). Do NOT pass an FBco ID; if you already have one, use the downstream tool directly. Uses tiered reso
• resolve_entity ~362 tokens
  Resolve an unresolved FlyBase-related query string into VFB/FlyBase IDs and metadata. Pass the raw text exactly as the user wrote it (for example "P{VT054895-GAL4.DBD}", "Hb9-GAL4", "SS04495", "MB002B", "PAM cluster", or "dpp"). Do NOT pass resolved IDs such as FBgn/FBal/FBti/FBco/FBst or VFB IDs; i
• run_query ~1185 tokens
  Run a pre-computed query on a VFB entity. REQUIRED WORKFLOW: (1) call get_term_info on the ID first; (2) read the response's "Queries" array; (3) pass one of those values as query_type. Calling run_query with a guessed query_type will return an error. If a query returns empty rows or an error, the e
• search_terms ~1120 tokens
  Search VFB terms. This is the search virtualflybrain.org itself runs — the same Solr query, the same ranking — so what comes back first here is what a user would see first on the site. USE filter_types BY DEFAULT. Unfiltered searches mix scRNAseq artifacts and developmental stages in with the entit

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