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Cure Cancer With AI

by io.github.hifarrer

Search pharmaceutical compounds

search_compounds

Find pharmaceutical compounds two ways: by example drugs you already know (fuzzy-matched), or by setting target characteristics on a −4…+4 scale. Provide exactly one of examples or preferences. Use list_compound_characteristics for the available preference names.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results (1–100, default 20).
examplesNoKnown drug names to find similar compounds for, e.g. ["aspirin","ibuprofen"]. Mutually exclusive with preferences.
preferencesNoMap of characteristic name to desired value on the −4…+4 scale, e.g. {"Neuroactive":3,"Immunoactive":-2}. Omit a characteristic to ignore it. Mutually exclusive with examples.
include_characteristicsNoInclude each result’s characteristic scores in the response.

TDQS

A4.1/5.0
Behavior3/5

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

With no annotations provided, the description carries full responsibility. It discloses fuzzy matching for examples and the -4 to +4 scale for preferences, but does not describe the return format, ordering, pagination, or other behavioral traits. Essential behaviors are communicated, but gaps remain.

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 exceptionally concise, consisting of two sentences that front-load the core functionality. Every sentence serves a distinct purpose, with no redundant or unnecessary words.

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

Completeness3/5

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

Despite the lack of an output schema, the description mentions the include_characteristics parameter affecting results. However, it does not specify what data is returned (e.g., compound IDs, names). Given the context signals (4 params, no output schema), more detail on return values would improve completeness.

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 description coverage is 100% (baseline 3). The description adds meaningful context beyond the schema, such as clarifying that examples are fuzzy-matched and that preference names come from another tool. This enhances understanding of parameter usage.

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's purpose: finding pharmaceutical compounds via two distinct methods (fuzzy-matched examples or characteristic preferences). It uses specific verbs and resources, and distinguishes itself from sibling tools like list_compound_characteristics by referencing them for supplementary information.

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 explicitly instructs to provide exactly one of `examples` or `preferences`, and directs users to list_compound_characteristics for available preference names. This provides clear guidance on when to use each mode, though it lacks explicit exclusion of other sibling tools like search_oncology.

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

A3.7/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: separate get/list for different data types (blog posts, clinical trials, research papers), distinct prediction tools (clintox, dti, ppi), and separate search tools (compounds vs. broad search). No two tools appear to overlap.

Naming Consistency4/5

Most tools follow the verb_noun pattern (e.g., get_blog_post, list_clinical_trials, predict_dti). The only outlier is mammal_health, which uses a different structure (noun_noun), causing minor inconsistency.

Tool Count5/5

With 15 tools, the server covers a broad oncology research domain without being overwhelming. Each tool serves a clear role, and the count feels well-scoped for the stated purpose.

Completeness4/5

The tool set covers retrieval and prediction for key domains (papers, trials, drugs, compounds) and includes a cross-dataset search. Minor gaps exist, such as the lack of a dedicated get_compound tool, but search_oncology can partially compensate.

Resources