Skip to main content
Glama

Cure Cancer With AI

by io.github.hifarrer

List cancer news

list_news

List curated cancer news articles aggregated from trusted sources. Filter by cancer type, keyword, or published 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, summary, and content.
cancerTypeNoFilter by cancer type, e.g. lung, breast, prostate, colorectal, melanoma, leukemia, lymphoma.

TDQS

A4/5.0
Behavior3/5

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

No annotations provided, so the description carries full burden. It mentions 'curated' and 'trusted sources' hinting at quality, but does not disclose ordering, pagination behavior beyond schema, rate limits, or any side effects. Adequate for a read-only list tool but leaves gaps.

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, 15 words, front-loaded with purpose. Every word earns its place; no fluff or repetition. Very efficient while covering key functionality.

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 6 optional parameters and no output schema, the description is fairly complete: it explains the source (curated, trusted) and filter dimensions. However, it does not describe what fields each article contains (e.g., title, summary) or default sort order, which would help an agent anticipate results.

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?

Schema description coverage is 100% with all 6 parameters described. The description redundantly mentions filtering by cancer type, keyword, or published date, which aligns with schema but adds no new meaning beyond the schema itself. Baseline 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?

Clearly states it lists curated cancer news articles, distinguishing it from sibling tools like list_blog_posts and list_clinical_trials. The verb 'list' and resource 'cancer news articles' are specific and unambiguous.

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?

Implicitly indicates usage for retrieving aggregated news with filtering options, but lacks explicit guidance on when to use this tool over alternatives such as list_blog_posts or list_clinical_trials. No when-not-to-use or prerequisites mentioned.

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

All Cure Cancer With AI toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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