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Cure Cancer With AI

by io.github.hifarrer

List research papers

list_research

List peer-reviewed oncology research papers ingested from PubMed (abstracts, authors, journal, plain-language summaries). Filter by cancer type, treatment type, keyword, or publication date.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNoISO date upper bound (e.g. 2024-12-31).
fromNoISO date lower bound (e.g. 2024-01-01).
limitNoResults per page (1–100, default 20).
offsetNoNumber of results to skip (default 0).
searchNoKeyword search across title and abstract.
cancerTypeNoFilter by cancer type, e.g. lung, breast, prostate, colorectal, melanoma, leukemia, lymphoma.
treatmentTypeNoFilter by treatment type.

TDQS

A3.6/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It states the tool lists papers with specific fields but omits behavioral traits like pagination (limit/offset exist in schema) or default ordering. The description does not cover mutation safety or rate limits.

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?

Two sentences with no superfluous words. The main action and key filtering capabilities are front-loaded, achieving maximal clarity with minimal length.

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

Completeness4/5

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

For a list tool with 7 parameters and no output schema, the description covers the source, content fields, and filter options. Minor gaps include pagination behavior and result order, but overall it is sufficient.

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%, earning a baseline of 3. The description adds value by specifying that 'search' applies to title and abstract and giving examples for 'cancerType', going beyond the schema's generic descriptions.

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 specifies a specific verb ('list'), resource ('research papers'), and scope ('oncology, ingested from PubMed'). It distinguishes this tool from siblings like 'get_research_paper' (single paper) and 'list_clinical_trials' (different resource).

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives like 'search_oncology' or 'list_news'. There are no explicit when-to-use, when-not-to-use, or alternative mentions, leaving the agent to infer context.

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