ResearchOracle - 11 financial research tools: 10-K parsing, equity, macro, citation graph.
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Average 3.3/5 across 11 of 11 tools scored. Lowest: 2.4/5.
Most tools target distinct resources (papers, authors, citations, DOIs). arxiv_search and search_papers both search for papers, but they are differentiated by corpus (preprints vs all). compliance_research is a convenience wrapper for compliance topics but is still distinct.
Tool names mix noun-noun (author_papers, citation_graph), noun-verb (arxiv_search, doi_lookup), and verb-noun (search_papers) patterns. While each name is readable, there is no consistent verb_noun convention, making it harder to predict tool names.
At 11 tools, the set is well-scoped for a research discovery platform. Each tool covers a necessary aspect: search, metadata, authors, citations, recommendations, trending, and system health.
The surface covers core research workflows: searching, retrieving details, author exploration, citation analysis, recommendations, and trending. Minor gaps exist (e.g., no journal-specific search or batch export), but agents can accomplish typical tasks without dead ends.
arxiv_searchCInspectSearch arXiv preprints — cutting-edge research before peer review.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | No | ||
| topic | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavior. It mentions 'cutting-edge research before peer review' but that's about the content, not the tool's behavior (e.g., result formats, rate limits, search syntax). The description does not explain what the agent should expect.
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, but it spends words on a marketing tagline ('cutting-edge research before peer review') rather than operational details. It could be restructured to include more useful information in the same space.
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 no annotations, no output schema, and zero parameter documentation, the description is insufficient. It explains the source but not how to use the tool, what parameters do, or what the agent will receive in response. Sibling context suggests overlap with 'search_papers' that is unaddressed.
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% for the three parameters (limit, query, topic). The description adds no meaning beyond parameter names. The agent receives no clues about how to use 'limit' or 'topic' in a query.
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 preprints, which is a specific resource. This distinguishes it from generic search tools, though it doesn't explicitly contrast with the sibling 'search_papers' tool. The verb+resource structure is solid.
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 'search_papers' or 'author_search'. The description implies it's for arXiv content but doesn't mention exclusions or context where other searches would be preferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
author_papersAInspectList papers by author. Use author_id from author_search.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| author_id | No |
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 states the basic function but omits any safety assurances (e.g., read-only), result ordering, pagination, error handling, or authentication requirements. The verb 'list' hints at a read operation, but this is 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?
Two concise sentences where the first states the core purpose and the second gives a direct usage hint. No irrelevant details or 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 description is too sparse given the lack of annotations, output schema, and parameter descriptions. It does not clarify that author_id is likely required (even though schema lists it as optional), mention the optional limit's effect, or describe the response structure. Significant gaps remain for agent to use it reliably.
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 0% schema description coverage, the description must compensate. The instruction to use author_id from author_search adds crucial context for the author_id parameter. However, the 'limit' parameter is completely unexplained, leaving its purpose (likely pagination) ambiguous.
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 uses a specific verb 'list' with resource 'papers' and qualifier 'by author', making the tool's purpose immediately clear. It naturally distinguishes from sibling tools like author_search (which finds authors) and search_papers (which searches generally).
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 an explicit prerequisite: 'Use author_id from author_search.' This guides the agent on how to obtain the required parameter and implies a workflow, indirectly separating this tool from author_search. However, it does not mention when not to use this tool or mention alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
author_searchAInspectFind researchers by name. Returns h-index, publications, affiliations.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Author name |
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 discloses return fields but does not state whether the operation is read-only, whether authentication is needed, or any limitations (e.g., exact vs fuzzy matching, multiple author disambiguation).
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 short sentences, with the action stated first and no redundant words. It efficiently communicates the core purpose and output, earning a high score for conciseness.
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 no output schema, the description provides a minimal list of return fields but omits important behavioral context like whether the query is required, result limits, or how multiple matches are handled. It is adequate for a simple tool but leaves 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?
The input schema already describes the only parameter 'query' as 'Author name', and the description repeats this by saying 'by name'. No additional semantic detail is added, such as format expectations or handling of empty queries, 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 finds researchers by name, using a specific verb and resource. Mentioning the return fields (h-index, publications, affiliations) distinguishes it from sibling tools like search_papers or citation_graph.
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 intended use is implied by 'Find researchers by name' but no explicit guidance is given on when to prefer this over alternatives like author_papers or citation_graph. No when-not-to-use conditions or alternative names are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
citation_graphCInspectExplore 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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior, but it only states the general function. It does not mention required input parameters (doi vs paper_id), the meaning of direction values, or the response format. No side effects or access requirements are disclosed.
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, efficient sentence that immediately conveys the tool's purpose. It is well-structured and front-loaded with the key concept, with no unnecessary words or repetition.
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 3 parameters and no output schema or annotations, the description is too sparse. It does not explain how to specify a paper (DOI or internal ID), what the response looks like, or the semantics of the direction parameter. The description leaves too much unspecified for successful invocation.
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 only 33% (only 'direction' has a bare list of possible values). The description does not explain the roles of 'doi' or 'paper_id', nor does it clarify how 'direction' relates to the cited/references phrasing. The tool description adds minimal semantic 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 identifies the tool as exploring citation networks, specifying both directions ('who cites a paper' and 'what it references'). It distinguishes itself from sibling tools like 'paper_detail' or 'search_papers' by focusing on citation relationships rather than general paper details or 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 is provided on when to use this tool versus alternatives. It does not mention any exclusions, prerequisites, or preferred contexts. The description merely implies use for citation exploration, but offers no explicit direction on when to choose it over related tools like 'paper_detail'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
compliance_researchAInspectOne-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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the bundle nature and the 20-topic limitation but does not mention return format, error handling, or rate limits, leaving some behavioral aspects undisclosed.
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 extremely concise at one line, with every phrase ('one-call bundle', 'peer-reviewed + preprints', '20 topics available') providing useful context without redundancy.
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 single-parameter tool with full schema coverage, the description adequately covers purpose and key constraint, though the absence of output schema means return details are unspecified. The simplicity of the tool makes the description sufficient.
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 schema already fully describes the 'topic' parameter with a list of valid values, so the description adds no additional semantic meaning beyond what the schema provides. 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 provides a combined bundle of peer-reviewed and preprints for compliance topics, distinguishing it from general search siblings like arxiv_search and search_papers. The verb is implicit but the function is unambiguous.
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 it (when you need both peer-reviewed and preprints in one call for a compliance topic) but does not explicitly state when not to use it or how it compares to alternative sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
doi_lookupAInspectLook up any paper by DOI via Crossref. Metadata, citations, journal.
| Name | Required | Description | Default |
|---|---|---|---|
| doi | No | e.g. 10.1016/j.frl.2024.105432 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of disclosing behavior. It mentions 'via Crossref' (external API) and lists return fields (metadata, citations, journal), but does not disclose potential rate limits, error handling, or whether it is read-only. This is moderate transparency for a simple lookup.
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 immediately states the tool's action and resource. Every word contributes value, with no redundancy or 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?
For a single-parameter, no-output-schema tool, the description covers the core purpose and expected outputs (metadata, citations, journal). It does not address edge cases like invalid DOIs or API errors, but the simplicity of the tool makes the description sufficiently 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?
The schema covers 100% of the parameter 'doi' with an example format. The description adds no extra semantic detail beyond reinforcing that it looks up by DOI, so it does not exceed the baseline set by high schema coverage.
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 uses the specific verb 'Look up' and clearly identifies the resource (paper by DOI via Crossref) and the expected outputs (metadata, citations, journal). It distinguishes itself from siblings like arxiv_search or author_papers by targeting DOI-based lookup.
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: use when you have a DOI and want paper details. It does not explicitly mention alternatives or when not to use, but the context is unambiguous for a DOI lookup tool, so it meets the 'clear context, no exclusions' criterion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
health_checkCInspectResearchOracle status, backends, coverage.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description must disclose behavior. It only lists subjects (status, backends, coverage) without stating whether the tool is read-only, what side effects exist, or what operations it performs. The health-check name implies safety, but the description does not say so.
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 extremely concise with no wasted words, front-loading the key subjects. It is a sentence fragment, but this is acceptable for such a simple tool. It scores slightly below 5 because it lacks a full-sentence structure.
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 no annotations, no output schema, and no parameters, the description must stand alone. It states what the tool checks but omits the return format, any prerequisites, and potential impacts. A sentence noting that it returns status information would improve 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?
The tool has zero parameters and an empty schema, so no parameter information is needed. The baseline for zero-parameter tools is 4, and the description does not need to compensate for missing parameter details.
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 identifies the tool as checking ResearchOracle status, backends, and coverage. Though it lacks an explicit verb, the name 'health_check' plus the specific resource and fields makes the purpose clear and distinguishes it from the research-paper sibling 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 usage context is provided. The description does not indicate when to call this tool, whether it is a prerequisite for other operations, or how it relates to sibling tools. It is a bare noun phrase with no guidance on alternatives or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
paper_detailBInspectFull paper details: abstract, TLDR, references, citations, PDF link.
| Name | Required | Description | Default |
|---|---|---|---|
| doi | No | ||
| paper_id | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses the output contents (abstract, TLDR, references, citations, PDF link), which is helpful, but it does not explain input requirements (e.g., whether doi or paper_id is needed) or possible error conditions.
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 with no filler. It efficiently conveys the tool's purpose and output components, earning every word's 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?
The tool is simple, but with no annotations, no output schema, and zero parameter documentation, the description leaves a significant gap: it does not clarify how to provide the paper identifier or whether both parameters are needed. It lists return contents, but lacks input guidance, making it incomplete for reliable invocation.
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% for both parameters, and the description provides no additional meaning for doi or paper_id. It does not indicate when to use one identifier over the other or that at least one is required, leaving the agent without essential parameter semantics.
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 function: retrieving full paper details, and lists specific components (abstract, TLDR, references, citations, PDF link). This distinguishes it from siblings like citation_graph or doi_lookup, which focus on different aspects.
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?
Usage is implied: use this when you need comprehensive details for a specific paper. However, there is no explicit guidance on when to choose this over alternatives like search_papers or citation_graph, nor are exclusions stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
paper_recommendationsCInspectAI-powered paper recommendations similar to a given paper.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| paper_id | No |
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 does not explain whether paper_id is required, how limit affects results, what happens if the paper is not found, or whether recommendations are ranked. This is a significant gap for a recommendation tool.
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, short sentence, which is concise and front-loaded. However, it is under-specified and does not earn its place with meaningful detail, so it is not as effective as it could be.
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, no annotations, and only two minimally described parameters, the description is far from complete. It omits return format, required inputs, and edge-case behavior, making it inadequate for an agent to invoke the tool correctly without additional assumptions.
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 schema has 0% description coverage for its two parameters. The description hints at paper_id via 'a given paper' but does not explain the limit parameter or clarify the relationship between them. It adds minimal value beyond the schema's bare type definitions.
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 provides paper recommendations similar to a given paper, which clearly identifies the action and resource. It distinguishes itself from siblings like search_papers and citation_graph by focusing on 'similar to a given paper,' though it lacks specificity about the output format.
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 citation_graph or paper_detail. The description does not mention prerequisites (e.g., needing a valid paper_id) or when this is the preferred choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_papersAInspectSearch 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 |
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 discloses the two search modes (topic and query), which is helpful. But it does not mention return format, pagination, or any limitations beyond the schema, leaving the agent with some uncertainty about the exact behavior.
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 leads with the core purpose and then includes actionable usage guidance. Every part earns its place with no redundancy.
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 no output schema and no annotations, the description could be more complete about what the response looks like or how results are ordered. It covers the main topic/query distinction but omits information about year filtering and output structure, which is a notable gap for a search tool.
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 adds meaningful context for 'topic' and 'query' by explaining their intended use, going beyond the schema's descriptions. It doesn't cover the undocumented year_from/year_to parameters, but those are reasonably inferable from their names, and the parameter coverage is moderate.
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 function with a specific verb and resource ('Search 200M+ scientific papers'). It distinguishes itself from siblings by mentioning both predefined compliance searches (topic) and free text (query), making its scope evident and differentiating it from 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?
The description gives explicit guidance on when to use 'topic' vs 'query' parameters, which is a clear usage direction. However, it does not compare with sibling tools or state exclusions, so it falls short of the highest score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
trending_researchBInspectMost-cited recent papers in a domain. Great for literature reviews.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | No | ||
| years | No | e.g. 2024-2026 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior, but it only states the output criterion ('most-cited recent papers') without explaining limitations, default behavior when no parameters are supplied, data source, or pagination. The 'Great for literature reviews' note is usage context, not behavioral transparency. This leaves significant ambiguity for an agent.
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 only two short sentences and is front-loaded with the primary function in the first sentence. Every word is relevant, with no fluff or repetition of the tool name.
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 list tool with no output schema and no annotations, the description provides a baseline level of understanding—what the tool returns and a key use case. However, it omits details about optional parameters, default behavior, and result format, which are important for correct invocation. It is adequate but not 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?
The input schema has only 50% description coverage: 'years' is exemplified with '2024-2026,' but 'topic' has no description. The description adds the term 'domain' which maps to 'topic,' but it does not clarify acceptable formats, whether parameters are optional, or how 'years' is interpreted beyond the example. The description fails to fully compensate for the schema gap.
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 identifies the tool's purpose as returning 'Most-cited recent papers in a domain,' which is a specific resource and filtering criterion. It distinguishes itself from sibling tools like paper_recommendations or citation_graph by emphasizing recency and citation count, though it lacks an explicit verb like 'list' or 'search.' Overall, the intent is unambiguous.
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 phrase 'Great for literature reviews' provides a clear use case and implies when an agent would choose this tool. However, it does not explicitly mention when not to use it or name alternatives such as search_papers or paper_recommendations, so it stops short of full usage guidance.
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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Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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