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biobtree_atlas

Curated Sugi Atlas knowledge for genes, diseases, and drugs (built from biobtree's own data).

SYNTAX: biobtree_atlas(entities=["TP53","imatinib"])

CALL THIS FIRST for a gene/disease/drug question (what it is, its biology, disease/drug/clinical context) - returns a concise, citable digest to ground your answer. Cite the returned canonical_url.

  • Pass the entity name(s) from the question; covered entities return content + citation, uncovered ones are listed in not_covered.

  • Default returns a compact digest (Summary + Identifiers). Each result lists the page's sections (top-level and sub-sections); pass section="Disease & clinical" (use a name from sections) for one zone, or full=true for the entire page (large). For big sections, query one entity at a time; full=true and large sections across several entities may be trimmed to fit.

  • For entities not covered, or for specific ID mappings / cross-references / filters, use biobtree_map / biobtree_entry instead.

RETURNS: per entity {type, canonical_url, content, sections} + not_covered list

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fullNoReturn the entire page (large; may exceed output limits - avoid for multiple entities)
sectionNoReturn one section instead of the digest; use a name from the result's `sections` (e.g. 'Disease & clinical')
entitiesYesGene symbols / disease names / drug names from the question

TDQS

A4.7/5.0
Behavior4/5

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

With no annotations provided, the description carries full responsibility and discloses key behavioral traits: returns a compact digest, lists not_covered entities, supports section/full modes, and notes potential output trimming for large requests. It could add more on error handling or rate limits, but it is notably transparent about return structure and performance caveats.

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?

The description is well-organized with clear labels (SYNTAX, CALL THIS FIRST, RETURNS) and front-loaded purpose. Every sentence contributes useful guidance—no filler or redundancy—while remaining compact for the complexity covered.

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 explains the return format per entity, the not_covered behavior, section selection, and the trimming caveat. It also references sibling tools for fallback, making it fully self-contained for an agent to invoke correctly.

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%, so baseline is 3, but the description adds meaningful context: entities are 'gene symbols / disease names / drug names from the question', section is 'a name from the result's sections', and full is cautioned as 'large; may exceed output limits'. This enriches the bare schema definitions.

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 provides curated knowledge for genes, diseases, and drugs, with a specific 'CALL THIS FIRST' directive for such queries. It distinguishes itself from siblings by explicitly naming alternatives for other use cases, making purpose and scope unambiguous.

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?

Usage guidance is explicit: 'CALL THIS FIRST for a gene/disease/drug question' and 'For entities not covered, or for specific ID mappings / cross-references / filters, use biobtree_map / biobtree_entry instead.' It also advises querying one entity at a time for large sections, reinforcing when and how to use the tool.

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

A4.4/5.0
Disambiguation5/5

Each tool has a distinct role: atlas for curated summaries, entry for full entry details, map for identifier mapping, and search for cross-database discovery. The descriptions clearly delineate when to use each, with workflow examples reducing any potential confusion.

Naming Consistency4/5

All tool names share the 'biobtree_' prefix and use lower_snake_case. The suffixes mix nouns (atlas, entry) and verbs (map, search), but this is consistent with their function and does not hinder readability or predictability.

Tool Count5/5

Four tools provide a well-scoped abstraction over 70+ databases. Each tool covers a distinct necessity: discovery, mapping, detailed lookup, and curated summaries. This is a solid, minimal set for the stated purpose.

Completeness5/5

The tool surface covers the full lifecycle of biological data exploration: search to find identifiers, map to translate between databases, entry to inspect connections and attributes, and atlas for citable summaries. No obvious dead ends; the workflow examples ensure agents can traverse the data graph effectively.

Resources