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togovar_search_disease

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Resolve a disease name to MONDO or MedGen identifiers for variant queries, prioritizing exact matches over loose token matches to improve search accuracy.

Instructions

Resolve a disease term to MONDO / MedGen IDs for variant search.

The returned mondo_id (or MedGen CUI) is what search_variant takes as disease_id. Both land directly on TogoMCP's existing mondo/medgen RDF databases and TogoID nodes.

The TogoVar endpoint does loose token matching with no relevance order (a query like "cystic fibrosis" also returns unrelated "Hepatic fibrosis…" rows), so this tool RE-RANKS client-side: exact label match first, then prefix, then whole-word, then loose token matches last. Each result carries match_type ("exact"|"prefix"|"word"|"fuzzy") — a top hit that is not exact means no exact label matched.

COVERAGE LIMIT: TogoVar only indexes diseases that have ClinVar/MGeND variant associations, so some canonical/parent MONDO terms are simply absent here (e.g. MONDO_0007254 "breast cancer" does NOT appear in these results). But a broad/parent MONDO ID still WORKS as a disease_id in search_variant even when unlisted here — the variant search resolves it via MONDO descendant expansion (MONDO_0007254 -> ~24,550 variants). So if you know or can resolve the canonical MONDO ID (e.g. via OLS4 or the mondo RDF database), pass it straight to search_variant; do not assume this resolver is exhaustive.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax matches to return, in [1, 100]. Default 10.
queryNoDisease term, e.g. 'breast cancer'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Beyond the readOnlyHint and openWorldHint annotations, the description discloses internal re-ranking behavior, match_type semantics, loose token matching pitfalls, and coverage limitations. It also clarifies that unlisted parent MONDO IDs still work downstream, adding crucial behavioral context.

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 longer than average but every paragraph adds substantive context: purpose, ranking logic, and coverage limitations. It is front-loaded with the core purpose and then provides necessary caveats, though some internal references (e.g., TogoMCP RDF databases) could be trimmed.

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?

Given the tool's complexity and the presence of an output schema, the description is exceptionally complete: it covers return field semantics, ranking order, edge cases, alternatives, and downstream integration. No critical behavioral aspect is left unexplained.

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 fully describes both parameters (`query` and `limit`) with types, ranges, defaults, and examples, so the description adds no new parameter-level detail. The baseline 3 is appropriate given complete schema coverage.

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 a disease term to MONDO/MedGen IDs for variant search, giving a specific verb and resource. It distinguishes itself from sibling tools by explicitly linking its output to `search_variant`'s `disease_id` parameter.

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?

The description explicitly explains when to use this tool: to resolve a disease term before calling `search_variant`. It also provides an alternative — using OLS4 or the `mondo` RDF database to resolve canonical MONDO IDs directly — and warns that the resolver is not exhaustive.

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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