# VirtualFlyBrain (remote · vfb3-mcp.virtualflybrain.org) MCP server for Drosophila neuroscience data from VirtualFlyBrain - Trust score: 59/100 (low) - Change this week: +3 - Registry status: active - Liveness: live - Owner verified: no - Last scored: 2026-08-03 ## Components - remote · `vfb3-mcp.virtualflybrain.org`: 59/100 (this document), [markdown](https://verifymcp.io/servers/org-virtualflybrain-vfb3-mcp/vfb3-mcp.md), [page](https://verifymcp.io/servers/org-virtualflybrain-vfb3-mcp/vfb3-mcp) ## Channel facts - Endpoint: `https://vfb3-mcp.virtualflybrain.org` - Transports: `streamable-http` - Auth: `none` - Version: `1.11.0` ## Trust breakdown How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog) Scored 2026-08-03. - **Endpoint Security**: 46/100 - The endpoint's TLS certificate is valid, in date, and uses a strong key. - Authorisation not fully verified: no authorisation is required to call this server, and 9 tool(s) never declared a destructiveHint. The MCP spec treats an absent hint as destructive by default, so we cannot call this surface safe. - HTTPS check failed: the endpoint is reachable over plaintext HTTP. - HSTS check failed: the Strict-Transport-Security header is absent. - DNSSEC check failed: this domain isn't protected by DNSSEC. - **Transport & Reachability**: 100/100 - Verified streamable-http transport via a live MCP handshake. - **Schema Quality & AI Usability**: 56/100 - AI-judged instruction clarity (excellent). - Context-footprint check failed: tool/resource definitions use about 4005 tokens (~445/item across 9 items; 9 tools + 0 resources), over budget; trim descriptions and params. - Usage-examples check failed: none of the tools include examples. - **Stability & Change Management**: 27/100 - Stability observed for 8 of 30 days with no destabilising changes; credit accrues until the full window elapses. - **Tool Coverage**: 100/100 - 100% of tools have a non-trivial description (not blank, and not just the tool's name). - 100% of tool parameters carry a description. - **Capabilities**: 100/100 - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28. ## Install ### Claude ```bash claude mcp add --transport http org-virtualflybrain-vfb3-mcp https://vfb3-mcp.virtualflybrain.org/ ``` ### Codex ```toml [mcp_servers.org-virtualflybrain-vfb3-mcp] url = "https://vfb3-mcp.virtualflybrain.org/" ``` ### opencode ```json { "$schema": "https://opencode.ai/config.json", "mcp": { "org-virtualflybrain-vfb3-mcp": { "type": "remote", "url": "https://vfb3-mcp.virtualflybrain.org/", "enabled": true } } } ``` ### OpenClaw ```bash openclaw mcp add org-virtualflybrain-vfb3-mcp --url https://vfb3-mcp.virtualflybrain.org/ --transport streamable-http ``` ### Hermes ```yaml mcp_servers: org-virtualflybrain-vfb3-mcp: url: "https://vfb3-mcp.virtualflybrain.org/" ``` ### Other ```json { "mcpServers": { "org-virtualflybrain-vfb3-mcp": { "type": "http", "url": "https://vfb3-mcp.virtualflybrain.org/" } } } ``` The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs. ## Changelog Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims. ### 2026-08-03 (score 59, +1) No change was recorded against any check on this day. Stability & Change Management went from 23 to 27. That category is still filling its 30-day observation window: 7 days of observed history at the previous scan, 8 at this one. The score rises as the window fills, whether or not the server changes. ### 2026-08-02 (score 58, +10) - [functional improvement] Schema quality: unverified → excellent ### 2026-08-01 (score 48, −11) - [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server ### 2026-07-31 (score 59, +1) No change was recorded against any check on this day. Stability & Change Management went from 13 to 17. That category is still filling its 30-day observation window: 4 days of observed history at the previous scan, 5 at this one. The score rises as the window fills, whether or not the server changes. ### 2026-07-30 (score 58, 0) - [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server ### 2026-07-29 (score 58, +1) No change was recorded against any check on this day. Stability & Change Management went from 7 to 10. That category is still filling its 30-day observation window: 2 days of observed history at the previous scan, 3 at this one. The score rises as the window fills, whether or not the server changes. ### 2026-07-28 (score 57, +1) No change was recorded against any check on this day. Stability & Change Management went from 3 to 7. That category is still filling its 30-day observation window: 1 days of observed history at the previous scan, 2 at this one. The score rises as the window fills, whether or not the server changes. ### 2026-07-27 (score 56, 0) - [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server ## MCP tools (9) ### `get_term_info` (~364 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 from a previous get_term_info call in this conversation. Returns: SuperTypes (classification), Tags (data flags like has_image, has_neuron_connectivity), Queries (valid query_types for run_query), RelatedTools (other MCP tools applicable to this entity, with default_args ready to copy — e.g. get_hierarchy with subclass_of for cell types or part_of for nervous-system regions), Images (keyed by template brain ID), Publications, Synonyms. Supports batch — pass an array of IDs to fetch in parallel; batch results are returned as a JSON object keyed by ID. To build VFB browser URLs from the Images field: https://v2.virtualflybrain.org/org.geppetto.frontend/geppetto?id=&i=,, — id= sets the focus term and i= lists images for the 3D viewer (template ID must be first in i= to set the coordinate space). Input parameters: - `force_refresh` (boolean): Bypass the response cache and recompute this result. Expensive — leave it unset on a first call. Set it ONLY to re-try a call that, earlier in this same conversation, returned a result that was clear… - `id` (required): One or more VFB IDs to look up ### `run_query` (~881 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 entity does not support that query_type or has no data for it — try a different query_type from the Queries array, or try a related entity (e.g. its parent class via get_hierarchy). Empty results do NOT mean the answer is unknown — only that this call did not return it. NEVER fabricate results from training data when a query is empty; tell the user clearly what was tried. NEVER pass tool names like "get_term_info" or "search_terms" as query_type — those are separate tools. Common query_types by entity kind: PaintedDomains, AllAlignedImages, AlignedDatasets, AllDatasets (templates); SimilarMorphologyTo, NeuronInputsTo, NeuronNeuronConnectivityQuery, NeuronRegionConnectivityQuery (individual neurons); ListAllAvailableImages, SubclassesOf, PartsOf, NeuronsPartHere, NeuronsSynaptic, ExpressionOverlapsHere, DownstreamClassConnectivity, UpstreamClassConnectivity (classes). Supports batch — pass an array of IDs (same query_type) or a "queries" array of {id, query_type} pairs; batch results are keyed by "ID::query_type". Results are PAGED: the first 25 rows by default (change with limit/offset) plus the true total as "count". ALWAYS read "count_status" before quoting "count": "exact" means count is the true total; "unavailable" means the query FAILED upstream and count is -1, which is NOT zero and must never be reported as "no results" — read "_note" and tell the user the query could not be run. Image/thumbnail columns are excluded by default to save space - pass include_images=true to include them. FlyBase integration is via query_types too: FindStocks (fly stocks for a FlyBase feature ID - FBgn/FBal/FBti/FBtp/FBco/FBst) and FindComboPublications (publications for an FBco split-… Input parameters: - `force_refresh` (boolean): Bypass the response cache and recompute this result. Expensive — leave it unset on a first call. Set it ONLY to re-try a call that, earlier in this same conversation, returned a result that was clear… - `id`: One or more VFB IDs to query - `include_images` (boolean): Include the image/thumbnail column in result rows. Default false: the thumbnail is a long markdown image string that is rarely useful to reason over and greatly inflates every row, so it is stripped… - `limit` (number): Max rows returned per call (default 25). The true total is always returned as "count"; broad queries (e.g. ListAllAvailableImages, or NeuronsSynaptic on a whole region) can have thousands to hundreds… - `offset` (number): Row offset for paging (default 0). To get the next page, re-run with offset increased by limit; "count" gives the total. - `queries` (array): Array of {id, query_type} pairs for mixed batch queries. When provided, id and query_type params are ignored. - `query_type` (string): A valid query type from the Queries array returned by get_term_info. Used for single id or array of ids. ### `search_terms` (~866 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 entity the user wants. Common filter_types recipes: \- Neuron classes: ["neuron", "class"] \- Individual neurons with images: ["neuron", "has_image"] \- Neurons with connectome data: ["neuron", "has_neuron_connectivity"] \- Brain regions / neuropils: ["anatomy"] \- Genes: ["gene"] \- Driver lines / expression patterns: ["expression_pattern"] \- Datasets: ["dataset"] There are over 200 type names and they change as data is added, so do NOT guess them: call list_search_facets to see the current vocabulary (optionally filtered, e.g. contains="lineage"). Names are matched case- and separator-insensitively, and a name that does not exist is an error with suggestions rather than a silently empty result. Deprecated terms are excluded by the search itself — you do not need exclude_types: ["deprecated"], and adding it is harmless but pointless. Stage filtering: VFB covers adult, larval, and embryonic data, and many anatomical FBbt classes are stage-agnostic. Do NOT add "adult" or "larva" to filter_types by default — only add them when the user is explicit about a stage (e.g. "adult Kenyon cells", "larval mushroom body"). Default searches should leave stage out so stage-agnostic classes and all life stages are visible. Useful flags: \- unique=true (the default) → one row per term. Turn it OFF only when you need to see WHICH synonym matched; with unique=false a term appears once per matching synonym, so "Kenyon cell" can return the same ID several times. \- minimize_results=true → top 10, essential fields only, for exploratory searches. \- auto_fetch_term_info=true → if an exact label match is found, returns get_term_info in the same response. \- boost_types=["has_image", "has_neuron_connecti… Input parameters: - `auto_fetch_term_info` (boolean): When true and an exact label match is found, automatically fetch and include term info in the response. - `boost_types` (array): Float results matching these facets_annotation types to the top of the ranked list without excluding others - `demote_types` (array): Sink results matching these facets_annotation types to the bottom of the ranked list without excluding them. Ignored for a type that also appears in boost_types. - `exclude_types` (array): Exclude results matching ANY of these facets_annotation types (OR logic). Deprecated terms are already excluded. - `filter_types` (array): Filter results to only include items matching ALL of these facets_annotation types (AND logic). Use list_search_facets for valid names. - `minimize_results` (boolean): When true, return at most 10 results with only the essential fields. For exact matches, return only the matching result. - `query` (string, required): Search query (e.g., medulla) - `rows` (number): Number of results to return (default 150, max 1000) - use smaller numbers for focused searches - `start` (number): Pagination start index (default 0) - use to get results beyond the first page - `unique` (boolean): One row per term (default true). Set false to get a row per matching synonym, which shows WHICH name matched at the cost of repeating IDs. ### `list_search_facets` (~166 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.g. contains="lineage" for the ~120 lineage clones, contains="connectivity" to find the connectome facets). The counts tell you whether a name is broad or niche — "entity" covers everything, a single lineage covers a handful. Input parameters: - `contains` (string): Only return type names containing this text. Matched case- and separator-insensitively, so "nervous system" finds "Nervous_system". ### `resolve_entity` (~311 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; if you already have an ID, use the downstream tool directly. Uses tiered resolution: exact name → synonym → broad pattern match. Returns match_type (EXACT/SYNONYM/BROAD), feature ID, name, type, and synonyms. IMPORTANT: When match_type is SYNONYM or BROAD, always confirm the resolved entity with the user before proceeding to further queries. If multiple matches are returned, show a disambiguation list and ask the user to choose. This tool queries FlyBase Chado — for VFB ontology lookups (anatomical terms, neuron class IDs) use search_terms instead. Input parameters: - `name` (string, required): Unresolved FlyBase-related query string from the user. Pass the raw name/synonym exactly as written (e.g., "P{VT054895-GAL4.DBD}", "Hb9-GAL4", "SS04495", "MB002B", "PAM cluster", "dpp"). Do NOT pass… ### `resolve_combination` (~218 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 resolution: exact name → synonym → broad pattern match. Returns FBco ID, combination name, matched synonym (if applicable), and component allele IDs/names. IMPORTANT: When match is via synonym, confirm the resolved combination with the user before proceeding (e.g., "Your search for 'MB002B' matched [formal name] (FBco...) via synonym. Shall I proceed?"). If multiple matches, show disambiguation list and ask user to choose. Input parameters: - `name` (string, required): Unresolved split-GAL4 combination name or synonym exactly as written by the user (e.g., "MB002B", "SS04495"). Do NOT pass an FBco ID here. ### `list_connectome_datasets` (~69 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. ### `query_connectivity` (~867 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 between two specific neuron classes (e.g., "What Tm1→T3 connections exist across all datasets?"). At least one of upstream_type or downstream_type is required. CONSTRAINTS: Only accepts neuron class terms (OWL IDs like FBbt_00003789 or labels like "transmedullary neuron Tm1") — anatomical regions or neuropils (e.g., "lobula", "medulla") are NOT accepted. NOT suitable for individual neuron-to-neuron connections — for pre-computed connections of a single individual neuron, use run_query with NeuronNeuronConnectivityQuery instead. NOT for muscle/sense organ connections. RECOMMENDED DEFAULTS: weight=5, exclude_dbs=["hb","fafb"] unless user specifies otherwise. For both-ends queries, start with weight≥50 to avoid timeouts. RESULT SIZE: a broad query is enormous (a single class at weight=5 can be over 50,000 connections), so results are ranked strongest-first and paged — you get limit rows (default 50) plus a summary computed over ALL of them: totals, per-dataset counts, distinct neuron counts, and the top class pairs. Answer from the summary and quote a handful of rows; only page with offset if the user asks for specific further rows. WORKFLOW: Confirm parameters with user before querying. Use search_terms with filter_types ["neuron","class"] to validate/canonicalize neuron type labels. If zero results, try relaxation: lower weight to 1, then remove exclude_dbs filter, then try group_by_class=true — report what worked and let user decide. group_by_class=true is usually the better first call on a broad query: it aggregates to class pairs instead of returning every neuron pair. Input parameters: - `downstream_type` (string): Downstream (postsynaptic) neuron class — OWL ID or full label. Must be a neuron type/class, NOT an anatomical region. If user asks about connectivity to a brain region, first find neuron classes in t… - `exclude_dbs` (array): Dataset symbols to exclude (recommended default: ["hb", "fafb"] to focus on newer datasets). Pass empty array [] to include all datasets. Must be the exact `symbol` field from list_connectome_dataset… - `group_by_class` (boolean): If true, aggregate results by neuron class — returns total_weight, average_weight, percent_connected per class pair, ranked by pairwise_connections. If false (default), returns individual neuron-to-n… - `limit` (number): How many connection rows to return, strongest first (default 50). The summary always covers every connection found, not just the returned rows. Pass 0 for all rows — only do this on a query you alrea… - `offset` (number): Row to start from within the strongest-first ranking (default 0). Re-running with the same limit and the next offset walks down the list. - `upstream_type` (string): Upstream (presynaptic) neuron class — OWL ID (e.g., "FBbt_00003789") or full label (e.g., "transmedullary neuron Tm1"). Must be a neuron type/class, NOT an anatomical region. Use search_terms with fi… - `weight` (number): Minimum synapse count threshold (recommended default: 5). Lower to 1 if initial query returns zero results as first relaxation step. ### `get_hierarchy` (~263 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?"). Descendants are returned as a nested tree for both relationship types. Ancestors are returned as a nested chain, filtered to nervous system terms for part_of. Start with max_depth=1 for direct parents/children, and offer to go deeper if the user wants more detail. Input parameters: - `direction` (string): Which direction to explore (default: "both") - `id` (string, required): VFB term ID (e.g. FBbt_00005801 for mushroom body, FBbt_00003686 for Kenyon cell) - `max_depth` (number): Number of levels to expand. 1 = direct children/parents only. Higher values go deeper. -1 = full tree (use with caution on broad terms). Default: 1. - `relationship` (string, required): Type of hierarchy: "part_of" for brain region structure, "subclass_of" for cell type taxonomies ## Diagnostics Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/org-virtualflybrain-vfb3-mcp/vfb3-mcp#diagnostics ## Score history - 2026-08-03: 59 - 2026-08-02: 58 - 2026-08-01: 48 - 2026-07-31: 59 - 2026-07-30: 58 - 2026-07-29: 58 - 2026-07-28: 57 - 2026-07-27: 56 - 2026-07-26: 56 ## Links - Remote endpoint: https://vfb3-mcp.virtualflybrain.org/ - Repository: https://github.com/Robbie1977/VFB3-MCP - Website: https://virtualflybrain.org/ - Changelog RSS feed: https://verifymcp.io/servers/org-virtualflybrain-vfb3-mcp/vfb3-mcp/changelog.xml - Changelog JSON feed: https://verifymcp.io/servers/org-virtualflybrain-vfb3-mcp/vfb3-mcp/changelog.json - HTML version of this page: https://verifymcp.io/servers/org-virtualflybrain-vfb3-mcp/vfb3-mcp