Skip to main content
Glama

VirtualFlyBrain

by org.virtualflybrain

resolve_entity

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 Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesUnresolved 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 an already resolved FlyBase or VFB ID.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.7/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses the tiered resolution process (exact name → synonym → broad pattern match), return fields, and confirmation requirements for SYNONYM/BROAD matches, including disambiguation steps. While not covering rate limits or error cases, it does provide substantial behavioral context beyond the schema (which has no annotations).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Every sentence adds value: it covers purpose, usage rules, examples, resolution logic, confirmation steps, and sibling differentiation without redundancy. The structure flows logically from what to pass, to how resolution works, to what to do with results.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no output schema and no annotations, the description is comprehensive: it explains the input format, resolution behavior, potential match types, user confirmation rules, and the difference from sibling tools. An agent would know exactly when and how to use it, and what to expect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the description enriches the parameter definition with multiple concrete examples and explicit negative constraints (do not pass resolved IDs). It also explains how the parameter value affects resolution behavior (tiered matching), adding meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific verb and resource: 'Resolve an unresolved FlyBase-related query string into VFB/FlyBase IDs and metadata.' It distinguishes from siblings by explicitly directing VFB ontology lookups to search_terms instead.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit when-to-use guidance: pass raw text exactly as written, do NOT pass resolved IDs, and use downstream tools if an ID is already available. It also names the alternative tool (search_terms) for different query types.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All VirtualFlyBrain toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.7/5.0
Disambiguation5/5

Each tool targets a distinct operation: searching vs. metadata retrieval vs. pre-computed queries vs. live connectivity vs. ID resolution vs. listing datasets/facets. The descriptions explicitly clarify differences between similar tools like query_connectivity and run_query, and resolve_entity vs. resolve_combination.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern (get_hierarchy, list_connectome_datasets, query_connectivity, resolve_entity, run_query, search_terms). No mixing of styles or vague verbs.

Tool Count5/5

9 tools is well-scoped for a specialized domain server. Each tool covers a necessary capability without redundancy or bloat, fitting comfortably within the ideal 3-15 range.

Completeness5/5

The tool set comprehensively covers the VFB domain: search, metadata, hierarchy, pre-computed queries, live cross-dataset connectivity, ID resolution, and listing of datasets and facets. Obvious gaps like direct image retrieval are handled via get_term_info, and publication queries are available through run_query.

Resources