AI/ML research papers from arXiv, DBLP, and HuggingFace
Scored across 3 tools
The three tools are distinguished by their source (HuggingFace, arXiv, DBLP), but get_ai_papers and search_arxiv_ai both support keyword search, creating some potential overlap. However, the different databases and the additional trending capability of get_ai_papers reduce ambiguity.
Names mix verb styles ('get' vs 'search') and include inconsistent modifiers ('ai' in two, absent in the third). The pattern is not uniform, though each name is still readable and indicates the source.
Three tools is on the lean side but appropriate for a focused research-paper search server covering three major sources. It feels slightly thin but not inadequate.
Covers the core need of finding AI/ML papers across major bibliographic sources (HuggingFace, arXiv, DBLP). Missing features like fetching full text or author-specific queries, but the main search workflows are present.
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) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the two modes (trending by date, search by keyword) but does not mention response format, rate limits, or other behavioral details. 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 two sentences, front-loaded with the core purpose and followed by a concise clarification of the two return modes. Every word earns its place; no fluff.
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 tool has 4 optional parameters, no required parameters, no output schema, and no annotations. The description explains the main modes but does not mention result format, defaults beyond schema, or how parameters interact (e.g., q needed for search). It is adequate for a simple read tool but leaves some gaps.
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 four parameters. The description adds context about the two modes ('type=trending' vs 'type=search') but does not introduce new semantic meaning beyond what the schema provides. Baseline 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 tool's purpose: getting trending or searched AI/ML research papers from HuggingFace Papers. The verb 'Get' and the source 'HuggingFace Papers' clearly distinguish it from the sibling tools (search_arxiv_ai, search_dblp) which target different repositories.
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 when to use the tool: when trending or searching HuggingFace papers. However, it does not explicitly contrast with alternatives (e.g., 'for arXiv use search_arxiv_ai'), leaving the differentiation to the user's inference from the source name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 |
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.
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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full transparency burden. It discloses that the service is free and requires no key, and that it searches both publications and authors. However, it omits details like rate limits, pagination, and return 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 two concise sentences, front-loaded with the core action, and contains no redundant information. It is appropriately sized for a simple search tool.
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 low-complexity tool with a complete schema, the description covers basic purpose and a key practical detail (no key required). However, it lacks guidance on when to use this tool versus siblings and does not mention the expected result shape. It is adequate but not thorough.
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 each parameter described in the input schema. The description adds no parameter-level meaning beyond the schema, so the baseline score of 3 applies.
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 DBLP for publications or authors, using a specific verb and resource. It doesn't explicitly contrast with sibling tools, but the unique resource name provides implicit differentiation.
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 given on when to prefer this tool over siblings like get_ai_papers or search_arxiv_ai. The only usage hint is 'Free, no key required,' which is a practical benefit but not a selection criterion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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