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resolve_combination

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 Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesUnresolved split-GAL4 combination name or synonym exactly as written by the user (e.g., "MB002B", "SS04495"). Do NOT pass an FBco ID here.

Schema Changelog

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

  1. Added

TDQS

A4.4/5.0
Behavior5/5

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

With no annotations to rely on, the description fully discloses the tiered resolution process (exact name → synonym → broad pattern match), the confirmation requirement for synonym matches, disambiguation for multiple matches, and the return fields. This is rich behavioral detail beyond simple function naming.

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

Conciseness4/5

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

The description is logically structured from purpose to input rules to behavior to edge cases, and is appropriately detailed. The quoted confirmation prompt is illustrative but not excessive, making it a solid, well-organized description.

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?

Despite lacking an output schema, the description enumerates all returned fields (FBco ID, combination name, matched synonym, component allele IDs/names) and explicitly handles the two key edge cases (synonym confirmation and multiple matches). For a tool with a single parameter, this is comprehensively sufficient.

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

Parameters3/5

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

The schema already describes the 'name' parameter with 100% coverage, including the exact instructions to pass raw text and avoid FBco IDs. The description merely repeats these instructions without adding new semantic nuance, so the baseline score of 3 applies.

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 the tool resolves split-GAL4 combination names or synonyms into FBco IDs and hemidrivers, with a specific verb and resource. It distinguishes from general resolver tools like resolve_entity by focusing on the split-GAL4 domain and explicitly excluding FBco IDs as input.

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

Usage Guidelines4/5

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

The description provides explicit when-to-use guidance (unresolved combination text) and a clear exclusion: do not pass an FBco ID, use the downstream tool directly instead. While no alternative sibling tool is named, the directive is unambiguous.

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

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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.