SaaS Browser

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

With no annotations provided, the description carries the full burden. It adds useful behavioral details (returns up to 25 published alternatives with profile URLs, descriptions, and names), but it does not disclose potential rate limits, authentication requirements, or error behaviors. This is adequate but not rich for a no-annotation 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 concise and front-loaded, with two sentences that directly state the purpose and output. Every sentence earns its place, and there is no fluff or redundant detail.

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 or annotations, the description is mostly complete: it defines purpose, input scope, and return format. It lacks explicit calling context or exclusions, but given the low complexity, it does not leave major 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 schema has 100% coverage for the single 'host' parameter, including examples. The description mentions 'given website host' but does not add meaning beyond the schema. It does not introduce format constraints, validation rules, or edge cases, so it meets the baseline for 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 clearly states the tool finds alternative/competing SaaS or software products for a given website host. It specifies the verb 'Find', the resource 'alternative/competing SaaS or software products', and the scope 'given website host', distinguishing it from sibling search tools that target categories, SaaS, and technologies.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool: when a user wants alternative products for a website host. It provides clear context and a concrete example of the host parameter, but it does not explicitly state when not to use it or mention exclusion criteria relative to sibling tools.

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 matching category IDs, which is useful for the downstream filter use case. However, it doesn't specify matching semantics (e.g., exact vs. fuzzy), pagination, or error behavior. This is acceptable for a simple search but leaves some behavioral ambiguity.

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, starts with the action, and wastes no words. It clearly states what the tool does and its intended use, with no redundant information.

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?

There is no output schema, so the description appropriately explains the return value ('matching category IDs') and its purpose (for SearchSaasTool's category_ids filter). For a simple one-parameter search tool, this is sufficient, though it could specify whether the output is a list/array or individual IDs.

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 single parameter 'q' with 'Search query', but the description adds nuance by clarifying that the query matches 'by name or keyword', enhancing the semantic meaning beyond the raw schema. Since schema coverage is 100%, the baseline is 3, and this added context justifies a 4.

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 ('Search') and resource ('SaaS Browser categories') and clearly differentiates from sibling tools by stating that it returns category IDs for use with SearchSaasTool's category_ids filter. This distinguishes it from SearchSaasTool, SearchTechnologiesTool, and SearchAlternativesTool.

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 indicates when to use the tool: to find matching category IDs for filtering results in SearchSaasTool. It implies the context (searching categories) and provides a clear alternative reference (SearchSaasTool), though it doesn't explicitly state exclusions 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.

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

With no annotations provided, the description carries the responsibility for behavioral disclosure. It discloses useful behaviors: the dataset size, a hard result cap of 25, and the inclusion of profile URLs. However, it does not address read-only guarantees, default sorting, pagination limits, or behavior when no query is supplied.

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, front-loaded sentence that moves from action to resource to result. Each clause contributes a distinct piece of information, and the vague 'and more' is acceptable shorthand for a tool with 38 optional parameters.

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 selection facts: it searches companies and returns a bounded set of enriched results (profile URLs). Given the strong 100% schema coverage and no output schema, an agent can usually invoke it correctly, but there are moderate gaps around result structure beyond URLs, sorting defaults, and how to obtain valid enum/vocabulary values.

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 met by the schema itself. The description adds a high-level grouping of filter dimensions ('category, technology, country, pricing, domain rating, employees, age, and more') but no new syntax or semantic details beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly identifies a search verb and the target resource ('SaaS Browser database of 400k+ SaaS companies') and states the result shape ('up to 25 results with profile URLs'). The tool is recognizable as a company-search engine, but there is no explicit differentiation from sibling tools such as SearchAlternativesTool.

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 conveys when to use this tool—when the task requires searching/filtering SaaS companies in the SaaS Browser database. It does not mention alternatives, exclusions, or cases where a sibling tool would be more appropriate, so usage guidance is only implicit.

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 output is a list of technology IDs (not full objects) and that search is by name or category, which is useful behavioral context. However, it does not mention rate limits, auth requirements, or any potential side effects, but for a simple search tool that is acceptable.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two concise sentences with no wasted words. It front-loads the verb and resource, then explains the return value and its purpose, making it well-structured and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a low-complexity tool with one parameter and no output schema, the description is complete. It states the search criteria, the return type (IDs), and how those IDs should be used, covering all essential information for correct invocation and downstream utilization.

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 100% coverage with 'q' described as 'Search query', but the description adds semantic value by specifying that the query can match by name or category. This goes beyond the schema's generic description and gives the agent clearer guidance on what to input.

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 SaaS Browser technologies by name or category and returns technology IDs for use with SearchSaasTool. This specific verb+resource+output purpose distinguishes it from sibling tools like SearchAlternativesTool and SearchCategoriesTool.

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 for use: it returns IDs intended for the SearchSaasTool technology_ids filter. This implies a concrete workflow but does not explicitly name alternatives or state when not to use this tool, so it falls short of a 5.

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