researchoracle
Server Details
ResearchOracle - 11 financial research tools: 10-K parsing, equity, macro, citation graph.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.2/5 across 11 of 11 tools scored. Lowest: 2.4/5.
Each tool targets a distinct aspect of scientific research: search types are separated (arXiv vs general), author discovery and paper listing are distinct, and utilities like citation graph, recommendations, and compliance bundle have clear unique purposes. No overlap.
All 11 tools use consistent snake_case with a noun_verb or descriptive pattern (e.g., arxiv_search, author_papers, doi_lookup). No mixing of styles or vague verbs.
11 tools is well-scoped for a research oracle covering search, author, paper details, recommendations, citations, trending, and health. Each tool earns its place without excessive specialization.
Core research workflows are covered: search, author lookup, paper details, citations, recommendations, and trending. Missing a tool for user-specific saved libraries or direct API key management, but that's not essential for the stated purpose.
Available Tools
11 toolsarxiv_searchCInspect
Search arXiv preprints — cutting-edge research before peer review.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | No | ||
| topic | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure, but it only mentions 'search' without indicating if it's read-only, what results look like, or any side effects. Minimal transparency.
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 very short (13 words), which is concise but to the point. However, it sacrifices necessary detail for brevity.
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 three undocumented parameters and no output schema or annotations, the description is far from complete. It provides no information on return values, pagination, or parameter constraints.
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 0%, and the description does not explain any of the three parameters (limit, query, topic). This leaves the agent with no understanding of how to use the parameters effectively.
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 it searches arXiv preprints and adds context about cutting-edge research. However, it does not explicitly differentiate itself from sibling tools like search_papers, which may have overlapping functionality.
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 alternatives like search_papers or author_search. The description lacks any context about appropriate use cases or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
author_papersCInspect
List papers by author. Use author_id from author_search.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| author_id | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It only states it lists papers, with no mention of side effects, authorization, rate limits, or output behavior. Critical behavioral traits like pagination or ordering are omitted.
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 very concise at two sentences and front-loaded. However, it sacrifices important information for brevity, making it less helpful than it could be while still being readable.
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 two parameters and no output schema or annotations, the description should explain what the list includes, how limit works, and any ordering. It fails to provide sufficient context for an agent to use the tool correctly without additional knowledge.
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 0%. The description adds that author_id comes from author_search, which hints at its source but not format. The 'limit' parameter is completely unexplained. The description provides minimal value beyond the parameter names.
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 'List' and the resource 'papers by author', making the purpose specific. While it hints at dependency on author_search, it does not explicitly differentiate from sibling tools like search_papers, but the core action is clear.
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 a usage hint by instructing to use author_id from author_search, implying a prerequisite step. However, it does not specify when to prefer this tool over alternatives like paper_detail or search_papers, or any exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
author_searchAInspect
Find researchers by name. Returns h-index, publications, affiliations.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Author name |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description partially compensates by stating it returns h-index, publications, and affiliations. However, it lacks details on search matching behavior (exact/fuzzy), what happens if no results, and any side effects (none expected).
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 tool's purpose and outputs. Every word adds value, with no unnecessary content.
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 simple schema (1 param) and no output schema, the description covers the basic purpose and returns. However, it omits details on result pagination, error handling, and whether the search is exact or fuzzy, leaving gaps for an agent.
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?
The input schema has 100% coverage with a description for the 'query' parameter ('Author name'). The description adds no additional meaning beyond that, so it meets the baseline but does not exceed it.
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 finds researchers by name and specifies returned fields (h-index, publications, affiliations), distinguishing it from sibling tools like 'author_papers' which likely retrieve papers for a known author.
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 vs alternatives such as 'arxiv_search' or 'doi_lookup'. The description does not mention when not to use it or provide context for its appropriate use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
citation_graphBInspect
Explore citation network: who cites a paper and what it references.
| Name | Required | Description | Default |
|---|---|---|---|
| doi | No | ||
| paper_id | No | ||
| direction | No | citing,cited_by,both |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It only says 'explore citation network' but doesn't disclose authentication needs, rate limits, pagination, or data 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?
One sentence, front-loaded with purpose. No wasted words.
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?
No output schema, so description should explain return format. It doesn't. Also lacks constraints (e.g., behavior when both identifiers provided) and does not differentiate from paper_detail which may also show citations.
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 33% (only direction has a description). The description adds no explanation for doi, paper_id, or direction beyond the schema's minimal context. With low coverage, description should compensate but doesn't.
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 'explore' and the resource 'citation network', and specifies the two directions: who cites and what it references. This distinguishes it from sibling search and detail tools.
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 vs alternatives like paper_detail or doi_lookup. No if/then or when-not advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
compliance_researchAInspect
One-call bundle: peer-reviewed + preprints for a compliance topic. 20 topics available.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | No | dora,mica,aml,amlr,stablecoin,operational_resilience,ai_governance,agent_security,defi_regulation,cbdc,tokenization,cyber_resilience,regtech,suptech,esma,eba,psd2,eidas,gdpr,basel |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description minimally discloses that it bundles peer-reviewed and preprints. However, it omits details like rate limits, response structure, or behavior for invalid topics.
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?
Single sentence, concise, and front-loaded. No unnecessary words.
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 one-parameter tool, the description is adequate but lacks details on return format, ordering, or error handling. Minimal but not fully complete.
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 topic description listing allowed values. The description repeats '20 topics available' but adds no new semantic info beyond the schema.
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 it returns a bundle of peer-reviewed articles and preprints for a compliance topic, and mentions 20 predefined topics. This distinguishes it from general search tools like arxiv_search.
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. It does not mention prerequisites, limitations, or when to avoid using it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
doi_lookupAInspect
Look up any paper by DOI via Crossref. Metadata, citations, journal.
| Name | Required | Description | Default |
|---|---|---|---|
| doi | No | e.g. 10.1016/j.frl.2024.105432 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It does not disclose any behavioral traits such as authentication requirements, rate limits, error handling for invalid DOIs, or whether results are cached. The minimal description leaves important behavioral context unknown.
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 without unnecessary words. It is front-loaded and 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?
For a simple lookup with one parameter and no output schema, the description covers the basic purpose. However, it lacks details on return format, error handling, or any limitations. Given the simplicity, a score of 3 indicates adequacy but with room for improvement in completeness.
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% with a single parameter 'doi' having an example. The description adds the context 'by DOI' but does not provide additional semantic details beyond the schema. Baseline score of 3 is appropriate since schema already covers the parameter well.
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 action 'look up', the resource 'paper by DOI', and the source 'via Crossref', specifying what is returned (metadata, citations, journal). It distinguishes itself from sibling tools like arxiv_search or search_papers which serve different lookup purposes.
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 when a DOI is available but provides no explicit guidance on when to use versus alternative tools or when not to use it. For a single-purpose lookup, the context is somewhat clear but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
health_checkBInspect
ResearchOracle status, backends, coverage.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description should disclose behavioral traits like safety or side effects. It only lists what is checked but does not state whether it is read-only or any potential limitations.
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 concise at one sentence with minimal waste, but could be more clearly structured as a complete phrase.
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 no output schema and no annotations, the description gives a basic idea of return values but lacks detail on format or interpretation, leaving some ambiguity.
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?
With zero parameters, baseline is 4. The description adds meaning by specifying what the tool checks (status, backends, coverage), which is helpful beyond the empty schema.
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 indicates checking the status, backends, and coverage of ResearchOracle, which is a distinct purpose from sibling tools that focus on paper searches and author lookups.
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 use this tool versus alternatives; the description does not mention any conditions or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
paper_detailCInspect
Full paper details: abstract, TLDR, references, citations, PDF link.
| Name | Required | Description | Default |
|---|---|---|---|
| doi | No | ||
| paper_id | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description only lists the data returned. It does not disclose behavioral traits such as authentication needs, rate limits, or error handling for missing papers.
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 with no wasted words, but it could benefit from structured formatting or additional context. It is minimally concise.
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?
With two parameters, no output schema, and no annotations, the description is insufficient. It does not specify how to invoke the tool or what to expect in terms of return values or errors.
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?
The description does not explain the parameters. Although 'doi' and 'paper_id' are somewhat self-explanatory, the tool requires at least one, and the description adds no meaning beyond the field names.
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 returns full paper details including abstract, TLDR, references, citations, and PDF link. However, it does not distinguish from sibling tools like doi_lookup which may have similar functionality.
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 alternatives. There is no mention of prerequisites, such as requiring a DOI or paper ID, nor any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
paper_recommendationsCInspect
AI-powered paper recommendations similar to a given paper.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| paper_id | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions 'AI-powered' but does not explain how similarity is computed, whether the operation is read-only, or any other behavioral traits like rate limits or idempotency. This gap is significant for a tool with no 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?
The description is a single sentence with no wasted words. It is concise but could benefit from additional context without losing brevity, such as clarifying parameters or output format.
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 lack of output schema and low schema coverage, the description is incomplete. It does not describe the return format (e.g., list of paper IDs or details) or any additional context needed to use the tool effectively, such as how recommendations are ordered or filtered.
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 0%, meaning the input schema provides no documentation for the two parameters (limit, paper_id). The description only vaguely references 'a given paper' but does not clarify the purpose of 'limit' or the format of 'paper_id'. The description fails to compensate for the lack of parameter documentation.
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 provides recommendations similar to a given paper, using the verb 'recommendations' and specifying the resource 'paper'. It distinguishes from siblings like 'arxiv_search' or 'citation_graph' which focus on search or citations rather than similarity-based recommendations.
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 when you have a specific paper and want similar ones, but does not provide explicit guidance on when to use this tool versus alternatives, nor does it mention any prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_papersAInspect
Search 200M+ scientific papers. Use 'topic' for predefined compliance searches (dora, mica, aml, etc.) or 'query' for free text.
| Name | Required | Description | Default |
|---|---|---|---|
| sort | No | relevance,citationCount,year | |
| limit | No | Max 20 | |
| query | No | Free text search | |
| topic | No | Predefined: dora,mica,aml,amlr,stablecoin,operational_resilience,ai_governance,agent_security,defi_regulation,cbdc,tokenization,regtech,suptech,esma,eba,psd2,eidas,gdpr,basel | |
| year_to | No | ||
| year_from | No |
Tool Definition Quality
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 states the scale (200M+ papers) and the two search methods, but does not mention whether the tool is read-only, how it handles combined parameters (e.g., topic + query), or any rate limits. This leaves gaps in transparency.
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 with no redundancy. The first sentence establishes the tool's scope, and the second explains the main parameters. It is front-loaded and every sentence serves a purpose.
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 tool has 6 parameters, no output schema, and no annotations, the description provides the essential search modes but lacks details on parameter interactions (e.g., can topic and query be used together?) and does not describe the return format. This 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 description coverage is 67%, so the baseline is 3. The description adds value by explaining that 'topic' is for predefined compliance searches and lists the acceptable values. However, it does not add meaning for 'sort', 'limit', 'query' beyond what the schema provides, and the 'year_from' and 'year_to' parameters remain undocumented.
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: searching 200M+ scientific papers. It specifies two distinct search modes—'topic' for predefined compliance searches and 'query' for free text—making the verb and resource clear. Although sibling tools exist, the unique mention of compliance topics differentiates it sufficiently.
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 guidance on when to use each parameter: 'topic' for predefined compliance searches and 'query' for free text. It does not explicitly mention exclusions or alternatives among siblings, but the context is clear enough for an agent to decide which parameter to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
trending_researchAInspect
Most-cited recent papers in a domain. Great for literature reviews.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | No | ||
| years | No | e.g. 2024-2026 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It implies the tool filters by citations and recency but does not define what 'recent' means by default, how many results are returned, or any rate limits. Basic behavioral traits are indicated but important details are missing.
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 consists of two short sentences with no redundant information. Every word contributes to the purpose and usage context, making it highly efficient.
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 core functionality but lacks details on default behavior (e.g., default year range if years omitted), result count, and output format. Given the tool's simplicity (2 parameters, no output schema, no annotations), the description is minimally adequate but could easily provide more complete guidance.
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 50% (years has an example, topic has none). The description adds context that the tool returns papers in a 'domain' (topic) and 'recent' (years), but does not clarify parameter formats (e.g., is topic a free-form string? Case sensitivity?) or whether parameters are optional. It provides marginal added value beyond the schema.
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 'most-cited recent papers in a domain' and suggests its use for 'literature reviews.' This specific verb-resource combination ('trending research' essentially means highly cited recent papers) distinguishes it from sibling tools like arxiv_search or paper_recommendations.
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 says 'Great for literature reviews,' which gives a usage context but does not explicitly state when to avoid this tool or mention alternatives. No exclusions or comparisons to siblings like author_papers or search_papers are provided.
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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