Get a blog post
get_blog_postFetch a single blog post by slug, including the full article content.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Blog post slug, e.g. "immunotherapy-breakthroughs". |
get_blog_postFetch a single blog post by slug, including the full article content.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Blog post slug, e.g. "immunotherapy-breakthroughs". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries the burden. It clearly states this is a read operation that returns the full article content. However, it does not mention whether the slug is unique, any error conditions, 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence that is front-loaded with the action and resource. No unnecessary words.
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 fetch tool with one parameter and no output schema, the description explains the output includes full content. Could mention that the slug is required and unique, but overall adequate.
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?
The only parameter (slug) is described with an example in the schema. The main description also references 'by slug', reinforcing its role. Schema coverage is 100%, so the description adds extra context with the example.
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?
The description uses a specific verb ('Fetch') and resource ('single blog post') with the identifier mechanism ('by slug') and what's included ('full article content'). It clearly distinguishes from sibling tools like list_blog_posts.
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?
No explicit guidance on when to use this tool versus alternatives. While the name and description imply it's for fetching a single post by slug, there is no mention of when not to use it or comparison with list_blog_posts.
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.