AI Tools Directory

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 what the tool returns (the four statistic types), which is the primary behavioral trait. It doesn't describe side effects or response format, but for a read-only summary tool this is largely sufficient. The description provides more than a bare statement of purpose.

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 front-loads the core action ('Get summary statistics') and then enumerates the specific statistics. There is no wasted wording, and every element 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?

Given that there are no parameters and no output schema, the description provides enough information for an agent to understand what the tool does and what it will receive. It lists all the statistic categories, which is essentially the complete output content. It could mention the return format, but that's a minor gap for a no-argument summary 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 tool has zero parameters, so the baseline is 4. The description adds no parameter information, but none is needed. It correctly focuses on what the tool does rather than on parameters that don't exist.

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 ('Get') and resource ('summary statistics for the directory') and lists exactly what statistics are included (tool count, department count, pricing distribution, free-tier fact count). It clearly distinguishes itself from siblings like search_ai_tools or list_departments by focusing on aggregate metrics rather than listing or searching.

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 its use case (obtaining an overview of the directory) but does not explicitly state when to choose it over alternatives or when not to use it. It doesn't mention that for individual tool details one should use get_ai_tool or for filtering search_ai_tools. The usage context is self-evident but not explicitly articulated.

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 adds important context by stating that 'Free-tier facts are auto-extracted and unverified,' warning the agent that data quality is not guaranteed. This goes beyond the schema and meaningfully informs expectations.

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 filler. The primary purpose is front-loaded, and the caveat about unverified facts is placed at the end without bloating the text.

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

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

For a simple search/find tool with five optional parameters and no required input, the description communicates the core behavior and an important reliability caveat. It doesn't mention return format, but the tool's purpose is simple enough that this is a minor gap, especially given the comprehensive parameter 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?

Schema coverage is 100%, so the baseline is 3. The description adds value by grouping the boolean parameters as 'auto-extracted facts' and clarifying they are unverified, which gives the agent deeper understanding of what these filters actually represent beyond their schema 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 a specific verb and resource: 'Find AI tools with a free tier.' It clearly distinguishes the tool from siblings like search_ai_tools or get_ai_tool by scoping results to free-tier tools and optional free-tier-related filters.

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 establishes clear context: use this when the goal is finding AI tools with a free tier, optionally narrowed by free-tier attributes. It does not explicitly name alternatives or when-not conditions, but the intended use case is evident and distinct from 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?

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It goes beyond the schema by revealing the matching strategy ('exact match first, then fuzzy'), which is valuable context. However, it does not disclose not-found behavior, error handling, or what 'full record' concretely contains.

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?

A single sentence delivers the core action, resource, and matching behavior with no filler. The parenthetical adds important nuance without bloating the definition.

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 or annotations, the description covers the primary action and match behavior. It is slightly lean on return expectations (what exactly 'full record' contains) and missing-not-found handling, but these are minor for a get-by-name 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 coverage is 100%, so the baseline is 3. The description adds meaningful semantics beyond the schema by explaining how the name parameter is interpreted during matching ('exact match first, then fuzzy'), which helps the agent know that partial or imprecise names may still resolve.

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 ('Get') with a clear resource ('full record for one AI tool') and specifies the lookup mode ('by name'). It distinguishes itself from sibling search_ai_tools by emphasizing retrieval of a single full record by name rather than searching.

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 clearly implies this tool is for retrieving a single known tool by name, which separates it from search_ai_tools and find_free_ai_tools. However, it does not explicitly name alternatives or state when not to use it, 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.

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 states the action and output (list with counts) but does not mention any potential caveats like pagination, sorting, or absence handling. For a simple read-only list, this is adequate but lacks depth; a 3 reflects the minimum viable level.

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?

A single, front-loaded sentence with no redundant words. Every element ('list', 'all', 'departments', 'number of tools') earns its place, making it highly efficient for an agent 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?

Given the tool's simplicity (0 params, no output schema, no annotations), the description fully specifies what the tool returns (all departments with tool counts) and the scope ('all'). Nothing essential is missing for an agent to 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 tool has no parameters, so the schema is trivially complete. The description adds no parameter-specific information because none is needed. Baseline 4 applies since there is nothing to clarify.

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 ('List'), the resource ('all industry departments'), and the additional detail (number of tools in each). This distinguishes it from sibling tools like get_ai_tool (retrieves a single tool) and search_ai_tools (searches), so an agent can easily identify when to use it.

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 a directory overview with counts) but does not explicitly exclude alternatives or mention when not to use it. However, given the distinct purpose, the context is clear enough for an agent to decide without further 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 burden. It discloses the search behavior (free-text matching against name, description, tags) and the return fields, which is useful. However, it doesn't disclose default/limit behavior beyond the schema, pagination, or whether the query is required. The description adds some behavioral context but not rich detail.

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 concise sentence that front-loads the action and scope, then lists return fields. 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?

For a search tool with 4 optional parameters and no output schema, the description covers the search dimensions and return fields. It could mention that all parameters are optional and combinable, and clarify the default behavior, but the schema already covers parameter details. The reference to list_departments helps with the department parameter.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all four parameters. The description adds a bit of context by listing the return fields and mentioning the department parameter references list_departments, but it doesn't add significant meaning beyond the schema. 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 searches an AI tools directory with specific filter dimensions (free-text, industry department, pricing tier) and lists the return fields. It distinguishes itself from siblings like get_ai_tool (single tool retrieval) and list_departments (department list).

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 to search/filter the directory, and the department parameter references list_departments for valid values. It doesn't explicitly state when not to use it or name alternatives like find_free_ai_tools, but the context is clear enough for an agent to select it.

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