Search arXiv preprints by keyword or category. Returns title, abstract, authors, and PDF link. Best for AI, CS, physics, and biology research.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search keyword or phrase | |
| category | No | arXiv category (e.g. "cs.LG", "cs.AI", "cs.CL", "quant-ph", "q-bio.GN") | |
| maxResults | No | Max papers (default 25) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears the full burden of behavioral disclosure. It only describes the function and output, but does not disclose any behavioral traits such as rate limits, authentication requirements, or impact on data sources.
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 consists of two concise, front-loaded sentences with no unnecessary words. Every sentence adds value: the first states the action and returns, the second provides context for use.
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?
Given the tool has 3 parameters, no output schema, and no annotations, the description adequately covers the purpose, return content, and best-use domains. It lacks details on pagination, sorting, or error handling, but the core information is sufficient for basic usage.
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 the schema already documents all three parameters. The description adds minimal extra meaning beyond the schema, mainly repeating the keyword/category concept. Baseline score of 3 is appropriate.
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 clearly states the action ('Search arXiv preprints') and the resource ('arXiv preprints'), specifies the returned fields (title, abstract, authors, PDF link), and distinguishes from sibling tools by mentioning the domains it is best for (AI, CS, physics, biology).
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 provides usage context ('Best for AI, CS, physics, and biology research'), helping the agent decide when to use this tool. However, it does not explicitly mention when not to use it or suggest alternative tools for other domains.
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.
