Search NIH RePORTER for grant awards by topic, agency, or institution. Returns project title, abstract, award amount, PI names, and institution.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Research topic or keyword (e.g. "CRISPR gene therapy cancer") | |
| agency | No | NIH agency code (e.g. "NCI", "NIAID", "NHLBI") | |
| maxResults | No | Max grants (default 25) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. Beyond stating it searches for grants, it offers no behavioral details such as rate limits, authentication requirements, or what happens when no results are found. The description adds minimal transparency beyond the basic functionality.
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?
The description is a single, well-structured sentence that front-loads the purpose and immediately follows with the output details. Every word adds value, with no redundancy or filler.
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 search tool with three parameters and no output schema, the description adequately covers what is searched (grants), how to search (by topic, agency, institution), and what is returned. Missing details like pagination or error handling are acceptable for a simple search tool.
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 coverage is 100%, providing baseline parameter descriptions. The description adds value by listing the returned fields (title, abstract, award amount, PI, institution), which helps the agent understand what data to expect from the query. This goes beyond the schema's parameter descriptions.
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 explicitly states the tool searches NIH RePORTER for grant awards, with clear specification of the resource (NIH RePORTER), action (search), and returned fields (title, abstract, award amount, PI names, institution). This clearly distinguishes it from sibling tools like search_pubmed (biomedical literature) or search_arxiv (preprints).
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?
The description implies usage for grant searches but provides no explicit guidance on when to use this tool versus alternatives, nor does it mention when not to use it. Sibling tool names hint at different domains (e.g., grants vs. literature or jobs), but no direct comparison or context is given.
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.
Most tools target unique data sources or specific actions (e.g., search_zillow vs. get_zillow_property_details are clearly sequential). A few LinkedIn-related tools (find_linkedin_candidates vs. search_linkedin_employees) have overlapping purposes but their descriptions clarify distinct use cases. Overall, confusion is minimal and descriptions resolve ambiguity.
All tools follow a consistent verb_noun pattern in snake_case, using verbs like search, get, find, scrape, analyze, lookup, resolve, and verify. The pattern is predictable across the entire set, making it easy for an agent to infer function from name.
With 32 tools, the server exceeds the 'too many' threshold of 25+. While the broad scope of web data mining justifies some diversity, the count is unwieldy and could overwhelm an agent's selection process. A smaller, more focused set per domain would improve coherence.
The toolset covers a wide range of data retrieval needs: company research, real estate, job listings, academic research, and government records. For a read-only data aggregation service, there are no major lifecycle gaps, though some subdomains like social media scraping only cover Reddit and LinkedIn, missing other platforms. Overall, it is reasonably complete for its stated purpose.
