AI/ML Research Papers
Server Details
AI/ML research papers from arXiv, DBLP, and HuggingFace
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 3.2/5 across 3 of 3 tools scored.
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.
Available Tools
3 toolsget_ai_papersBInspect
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) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must carry the burden. It mentions default and max limit values, which is helpful, but lacks details on authentication, rate limits, or whether results are cached. The read-only nature is implied but not explicit.
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 efficiently conveys the tool's purpose and core functionality. No extraneous information, well 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?
The description covers the main purpose but lacks output schema or description of the return format. Given the presence of sibling tools, it does not fully contextualize when to use this specific source. However, for a simple retrieval tool it is minimally adequate.
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%, so baseline is 3. The description adds value by explaining the interaction between 'type', 'date', and 'q' parameters, clarifying the two modes (trending vs search) beyond individual 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 clearly states the tool retrieves AI/ML research papers from HuggingFace Papers, with two modes (trending and search). It distinguishes the source from siblings (search_arxiv_ai, search_dblp) but does not explicitly contrast them.
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 is provided on when to use this tool versus the sibling tools (e.g., when to choose HuggingFace Papers over ArXiv or DBLP). No exclusions or context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_arxiv_aiBInspect
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 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavior. It mentions returning recent submissions sorted by date but omits details like rate limits, pagination, error handling, or response format. This is insufficient for safe invocation.
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, front-loaded sentence that efficiently conveys the tool's purpose and key behavior. Every word adds value.
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 simplicity (3 params with full schema coverage, no output schema), the description is minimally adequate. However, it lacks details about return structure or data fields, which would help an agent interpret results.
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 descriptions cover all three parameters (q, limit, category). The description adds no extra insight beyond what is in the schema, so baseline 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 tool searches arXiv for AI/ML papers by keyword and category, which is specific. However, it does not explicitly differentiate from the sibling tool get_ai_papers, leaving ambiguity about which to choose.
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 like get_ai_papers or search_dblp. The description provides no context about prerequisites, appropriate scenarios, or when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_dblpBInspect
Search DBLP computer science bibliography for publications or authors. Free, no key required.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Publication search query | transformer attention |
| limit | No | Number of results (max 25) | |
| author | No | Search by author name instead of publication title |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only mentions 'Free, no key required', revealing auth needs. It fails to disclose rate limits, pagination, or behavior on empty results, lacking sufficient transparency for a tool with zero annotation support.
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?
Two succinct sentences with no superfluous words. Every part is earned: the action, the resource, and the key benefit (free, no key).
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?
Despite good schema coverage and simplicity, the description omits crucial details: it does not explain the dual-mode behavior (publication vs author search) or what the output looks like. A minimal but incomplete account for a tool with no output schema.
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 baseline is 3. The description adds no extra meaning beyond parameter names and defaults already in schema; it does not explain how 'author' switches search mode or other nuances.
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?
Description explicitly states 'Search DBLP computer science bibliography for publications or authors', providing a clear verb and resource. It distinguishes from siblings (get_ai_papers, search_arxiv_ai) by specifying DBLP, a distinct database.
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 explicit guidance on when to use this tool versus alternatives like search_arxiv_ai. The description implies usage for DBLP searches but lacks when-not statements or context for tool selection.
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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