list_blog_postsList editorial blog articles (excerpts). Use get_blog_post for full content. Filter by category, cancer-type tag, or keyword.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Results per page (1–100, default 20). | |
| offset | No | Number of results to skip (default 0). | |
| search | No | Keyword search across title, excerpt, and content. | |
| category | No | Filter by primary category. | |
| cancerType | No | Filter by cancer-type tag. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must cover behavioral traits. It mentions 'excerpts' implying partial content, but no details on side effects, auth, rate limits, or if read-only.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no wasted words. Purpose is front-loaded, and key filtering options and sibling guidance are included efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple listing tool with 5 optional parameters and no required fields, the description covers output nature (excerpts) and filters. Lacks sorting details but adequate for the complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so description adds no new meaning beyond schema. The mention of filtering aligns with schema but doesn't provide additional syntax or format details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states action ('List') and resource ('editorial blog articles (excerpts)'). Distinguishes from sibling 'get_blog_post' by noting it provides excerpts, not full content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly directs to 'get_blog_post for full content' and lists filtering options. Lacks broader context on when not to use, but sufficient given sibling tools are mostly clinical.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.