Get a research paper
get_research_paperFetch a single research paper by its internal id or PubMed id.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| idOrPubmedId | Yes | Internal id or PubMed id, e.g. "38123456". |
Cure Cancer With AI
TDQS
B3.4/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavior beyond purpose. It does not disclose that it is a read-only operation, lacks side effects, or specify behavior if the id is not found. Minimal transparency.
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?
A single sentence that is concise and direct, containing no unnecessary words or information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema is provided, and the description does not indicate what fields or structure the returned paper will have. For a fetch tool, this leaves the agent without expectations about the response.
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 coverage is 100% and the schema description for the single parameter is clear. The description repeats that the parameter accepts internal id or PubMed id, adding no new meaning beyond the schema.
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 action 'Fetch', the resource 'a single research paper', and the method 'by its internal id or PubMed id'. It effectively distinguishes from sibling tools like get_blog_post and list_research.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when an id is available but does not explicitly state when to use this tool versus alternatives, such as list_research for multiple papers. No guidance on choosing between id types or handling missing results.
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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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.