AskCyborg

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are present, so the description carries the burden. It implies a read-only comparison but does not explicitly state whether it modifies data, requires authentication, or has side effects. It does add context on comparison dimensions, which is useful.

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 sentences: one states the function, the second lists use cases. No redundancy or irrelevant details.

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 with one parameter and no output schema. The description adequately explains what it does and when to use it, covering the essential context for an agent to select and invoke it correctly.

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 describes the 'companies' parameter (names or slugs, 2-5 items). The description adds meaning by specifying what will be compared (Cyborg Score, industry, etc.), helping users understand the expected input's relevance 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 compares 2-5 companies on specific attributes (Cyborg Score, industry, key insights, competitive positioning). This distinguishes it from siblings like get_company_report (single company) and search_companies (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 provides clear use cases ('portfolio decisions, M&A short-listing, or competitive analysis'), offering context for when to use it. However, it does not explicitly name alternative tools or state 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.

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. It clearly states the return behavior (competitors with strategic taglines) and the source of the data. It does not mention limitations like data freshness or potential empty results, but for a simple read-only lookup, the key behavior is transparent.

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 wasted words. The first sentence immediately states the core purpose, and the second adds the return value and use cases. Every part contributes 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 tool with one parameter, no annotations, and no output schema, the description provides sufficient context: it names the input, explains the output (competitors with taglines), and suggests use cases. It stops short of detailing the exact response structure, but the simplicity of the tool makes this acceptable.

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 parameters with a clear description ('Company name, ticker, or slug'). The tool description adds no additional parameter-level detail, so the baseline of 3 is appropriate since the schema does the heavy lifting.

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 ('Find') and resource ('named competitors of a company') with a clear source ('AskCyborg's analyst panel'), distinguishing it from sibling tools like search_companies and compare_companies. It also specifies the output ('one-line strategic tagline').

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 explicit use cases ('Useful for competitive landscape analysis, M&A short-listing, or pricing reference checks'), giving clear context for when to use it. However, it does not explicitly state when not to use it or name alternatives, so it lacks the exclusionary guidance required for a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden of behavioral disclosure. It clearly states that the result is a paywall-aware summary, not the full 30-page report, and that the full report and analyst-debate audio are accessible via a returned URL. This provides useful context about output limitations and an action the user can take. It does not mention error conditions or auth requirements, but the core behavior is well described.

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 primary action, and every phrase adds value. It lists report components and the key behavioral nuance (paywall-aware summary) without unnecessary filler. Perfectly concise and well-structured.

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?

Even without an output schema, the description provides a thorough understanding of the return value by enumerating the report sections (executive summary, Cyborg Score, strategic profile, etc.) and the included URL for the full report. Given the tool's single parameter and moderate complexity, this description is complete enough for an agent to select and invoke the tool correctly.

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 the only parameter 'company' with a detailed description including examples and accepted forms. The tool description adds no additional parameter-level meaning beyond what the schema provides, so the baseline score of 3 applies. The description's mention of company focus is implicit in the report nature rather than 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 uses the specific verb 'Retrieve' and identifies the resource as 'AskCyborg's structured research report for a single company.' It clearly differentiates from sibling tools like compare_companies and get_cyborg_score by stating it is a comprehensive report for one company, listing included sections.

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 this tool: when a detailed, single-company research report is needed. It distinguishes from sibling tools by focusing on a single company and mentioning the full report vs. the summary. However, it does not explicitly state when NOT to use it or name alternatives for comparison or specific insights, so it lacks explicit exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are present, so the description carries the full burden. It implies a read-only operation via 'Retrieve' and adds context about the score's composite origin. However, it doesn't disclose error behavior, data freshness, or what happens if the company is not found, leaving some behavioral gaps.

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 three sentences, each earning its place: the first states the action, the second explains the proprietary nature of the score, and the third gives usage guidance. It is front-loaded and free of waste.

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 retrieval tool with no output schema and no annotations, the description covers the core purpose, usage context, and parameter guidance. It lacks details on return format and error handling, but these are less critical given the tool's simplicity. Overall, it is near 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 a single parameter 'company' described as 'Company name or ticker.' The description adds no additional parameter-level meaning beyond repeating the company focus, so it doesn't exceed the schema baseline.

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 'Retrieve' and names the exact resource: the Cyborg Score (1-10) for a company. It distinguishes itself from siblings like get_company_report by stating it's 'just the Cyborg Score' and contrasts with 'full report context', making the tool's scope unmistakable.

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 explicitly states when to use the tool: 'when the user wants a quick rating without the full report context.' This provides clear context, but it doesn't name alternative tools explicitly or list exclusions, so it stops just short of full guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 that entries are 'dated and concise' and that they are selected by AskCyborg's analyst panel as 'decision-relevant.' This adds meaningful context beyond the schema, explaining the curation process and return format. It does not mention potential limitations like pagination or recency windows, but for a simple retrieval tool, the transparency is strong.

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 three sentences, each earning its place. It front-loads the main purpose with the verb 'Retrieve,' then provides essential detail on entry format, and finally gives usage scenarios. There is no wasted text, and the structure aids quick comprehension.

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 only one parameter, no output schema, and no annotations, the description is largely sufficient. It explains what the tool returns (material developments, dated and concise), the types of developments included, and the curation context. It lacks an explicit statement that the result is a list or any limits on the number of entries, but these are implicitly inferable from the wording. For a tool of this simplicity, the description is complete enough.

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 the only parameter 'company' with a clear description: 'Company name, ticker, or slug.' Since schema coverage is 100%, the baseline is 3. The tool description does not add any additional information about the parameter beyond what the schema provides, so it neither improves nor worsens the score.

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 starts with a specific verb 'Retrieve' and clearly identifies the resource: 'most recent material developments' with enumerated types. It distinguishes itself from sibling tools like get_company_report or get_top_insights by focusing specifically on recent developments, making the tool's unique purpose obvious.

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 explicit use cases: 'news catch-up before a meeting, for monitoring portfolio companies, or to spot recent strategic shifts.' It does not explicitly mention alternatives or when not to use, but the contexts are clear and distinct from sibling tools, so it meets the bar for clear usage guidance without exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses that the tool returns top, punchy, decision-relevant claims and is faster than a full report, adding meaningful context. However, it does not describe return format, pagination, or other potential behaviors, leaving some gaps.

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 sentences, front-loaded with the core action, then a concise comparison and use case. Every sentence earns its place with zero fluff 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?

For a simple one-parameter tool with no output schema, the description provides essential context: what it returns, the benefit, and when to use it. It could mention the output format or structure slightly more, but it is adequately complete for the tool's simplicity.

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 only parameter 'company' with a clear description. The tool description adds no extra meaning to the parameter itself, so it appropriately relies on the schema. Baseline of 3 is justified.

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 'Retrieve' and a specific resource 'top analyst-debate insights for a company'. It clearly distinguishes itself from sibling tool get_company_report by emphasizing speed and focus, making the tool's unique purpose 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?

Provides clear context for when to use this tool ('when you just need what should I know about this company in 60 seconds') and explicitly names the alternative (get_company_report) with a comparative advantage. Does not explicitly state when not to use it, but the guidance is sufficient.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the burden of explaining behavior. It discloses the return format ('Cyborg Score and one-line strategic profile'), the ordering ('ranked by Cyborg Score'), and the limit behavior ('up to N companies'). It does not mention edge cases like no results or matching semantics, but for a read-only search tool, this is substantial coverage.

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: two sentences. The first sentence states the core action and scope; the second explains the output and enumerates three concrete use cases. Every sentence adds value, with no repetition of schema details.

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 two-parameter search tool with no output schema and no annotations, the description is complete. It explains what is returned (Cyborg Score, strategic profile), the ranking, and the use cases. The limit parameter is referenced via 'up to N companies', making the behavior clear without needing to restate the 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?

The schema already provides full coverage of both parameters (industry and limit) with descriptions and examples. The tool description adds no additional parameter-level meaning, so the 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 has a specific verb ('Find') and resource ('companies in AskCyborg's corpus'), and clearly scopes the operation to 'a specific industry'. It also distinguishes this tool from generic search and competitor tools by mentioning sector mapping, comparable analysis, and discovery, and by specifying the ranking by Cyborg Score.

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 explicitly provides use cases ('sector mapping, comparable analysis, or to discover companies you didn't know existed in a space') and clarifies it is for industry-based discovery. However, it does not explicitly state when not to use this tool or name alternative tools like search_companies or find_competitors.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 clearly states the operation is a search and discloses the return structure (company name, Cyborg Score, one-line strategic profile, URL). While it doesn't explicitly state it's read-only, that is implied by 'Search' and the described outputs, which is sufficient for a search 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 two sentences, front-loaded with the core purpose and return details, followed by usage guidance. Every sentence adds value and the result is compact and well-structured.

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 essential aspects: what it searches, what it returns, and when to use it. The parameters are simple and fully described in the schema, so no additional explanation is needed. It could benefit from a note on the limit behavior, but that is a minor gap for a straightforward 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?

Schema description coverage is 100%, so the baseline is 3. The description mentions 'by name or industry keyword' which aligns with the query parameter, but it does not add meaning beyond the schema. It doesn't mention the limit parameter at all, so it neither enhances nor detracts from 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 states 'Search AskCyborg's corpus of company research by name or industry keyword', which clearly identifies the verb, resource, and scope. It distinguishes from siblings by positioning itself as the tool to 'Use this first' for company research, making it the entry point before other specialized 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?

The description explicitly says 'Use this first when the user mentions a company you want to research, or to discover companies in a category', providing clear context and a preferred usage scenario. It does not explicitly name alternatives or exclusions, but the guidance is specific enough to direct appropriate tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.