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. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description must disclose behavior. It only says 'returns recent submissions sorted by date,' omitting details like rate limits, read-only nature, or result structure.
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 concise sentence that front-loads the verb and resource. No wasted words; every part earns its place.
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 tool with 3 parameters and no output schema, the description covers the basic action but lacks details on return fields or pagination. It is adequate but not comprehensive.
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% with descriptions for all 3 parameters (q, limit, category). The tool description adds no extra meaning beyond what the schema already provides, meeting the baseline.
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 states the tool searches arXiv for AI/ML papers using keyword and category, which is specific and clear. However, it does not differentiate from sibling tools like get_ai_papers or search_dblp, lacking contrast.
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 guidance on when to use this tool versus alternatives. The description implies general search use but provides no preconditions, exclusions, or context for choosing it over siblings.
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 targets a distinct data source: HuggingFace Papers, arXiv, and DBLP. Their purposes are clearly differentiated by source and search type, with no overlap.
All names use lowercase and underscores, but one uses 'get' while two use 'search', creating a slight inconsistency. Otherwise, the pattern is clear.
Three tools is appropriate for a research papers server, covering three major sources without being too few or too many.
The server covers searching across three key AI/ML databases, which is adequate for discovery. Minor gaps exist, such as the lack of a tool to fetch paper details by ID.