Search arXiv for AI/ML papers by keyword and category. Returns recent submissions sorted by date.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Keyword search query | large language model |
| limit | No | Number of results (max 20) | |
| category | No | arXiv category: cs.AI, cs.LG, cs.CL, cs.CV, cs.RO, stat.ML (default: cs.LG) | cs.LG |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It does reveal that results are 'recent submissions sorted by date,' which is useful. However, it omits details about response structure, pagination, or any API requirements. For a read-only search tool, this is adequate but not comprehensive.
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 immediately conveys the core function and return behavior. There is no redundancy or unnecessary detail, making it highly concise and front-loaded.
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 search tool with fully documented parameters, the description covers the essential purpose and return ordering. Since there is no output schema, it could be more complete by mentioning result fields, but the current level 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 coverage is 100%, meaning all parameters have descriptions, so the baseline is 3. The description's mention of 'keyword and category' aligns with the q and category parameters but adds no new semantics beyond what the schema already provides.
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 tool's purpose: 'Search arXiv for AI/ML papers by keyword and category.' It also mentions the return behavior ('Returns recent submissions sorted by date'), which adds clarity. However, it does not explicitly differentiate from sibling tools like get_ai_papers, so it stops short of a 5.
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 clear context: this tool should be used when searching arXiv for AI/ML papers. It does not mention exclusions or alternative tools, which would be needed for a 5, but the intended use is clear enough for an agent.
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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