Get trending or searched AI/ML research papers from HuggingFace Papers. Returns trending papers for a given date or search results by keyword.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Search query (for type=search) | |
| date | No | Date for trending papers (YYYY-MM-DD, default: yesterday) | |
| type | No | trending or search (default: trending) | trending |
| limit | No | Number of results (max 25) |
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?
No annotations are provided, so the description carries full burden. It only restates the parameter-driven behavior already covered by the schema (returns trending papers for a given date or search results by keyword) without disclosing additional behavioral traits like pagination, rate limits, or expected response format.
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 sentence that begins with the verb and purpose. It is concise with no redundant information.
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 4 parameters, no output schema, and no annotations, the description is adequate but leaves gaps. It does not explain the mutual exclusivity of 'q' and 'date' relative to 'type', nor does it describe the response structure.
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 baseline is 3. The description adds no new meaning beyond the schema; it mentions 'trending' and 'search' but does not clarify parameter relationships or constraints.
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 verb 'Get' and the resource 'trending or searched AI/ML research papers from HuggingFace Papers'. It distinguishes the tool from siblings (search_arxiv_ai, search_dblp) by specifying the source as HuggingFace Papers.
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 scenarios (trending by date or search by keyword) but does not explicitly state when to use this tool versus the sibling tools. There is no when-not guidance or alternatives mentioned.
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.