directory

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

No annotations are provided, so the description must carry the behavioral burden. 'Get' clearly implies a read operation, and the description is transparent about what the returned record contains. It does not discuss not-found behavior or auth requirements, but for a simple slug-based getter this is a minor gap.

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, well-structured sentence that front-loads the action and resource, then packs the key record fields into a parenthetical. Every element adds useful information with no repetition or filler.

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 one required parameter, no annotations, and no output schema, the description covers the essential context: the lookup key and the content of the returned record. It could mention what happens if the slug does not exist, but the tool is simple enough that the description is otherwise 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 provides only 'slug' with type string and no description, so the description's 'by its slug' adds the essential meaning: the slug is the unique locator for the affiliate program. It does not give format examples or constraints, but for a single identifier parameter this is sufficient.

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 a clear resource ('full verified record for one affiliate program'), identified by slug. It also enumerates the record's contents, distinguishing it from broader search or list 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 establishes clear context: use this tool when you already have a specific affiliate program slug and need the full record. It does not explicitly name alternatives like search_affiliate_programs or state when not to use them, but the singular-by-slug framing makes the intended use obvious.

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 usefully discloses that this returns aggregate headline numbers rather than detailed records, and even specifies a verification-count metric. It does not mention whether the data is cached, live, or limited in any way, but for a simple stats read the transparency is adequate.

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, immediately readable sentence. The phrase 'Headline coverage' front-loads the aggregate nature, and the rest of the sentence lists the exact metrics. No filler 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 zero-parameter tool with no output schema and no annotations, the description tells an agent what it will learn: total programs, categories, networks, and verified-against-source count. It could add the return shape, but the core information needed to select and invoke the tool correctly is present.

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 100% schema description coverage, so the baseline is 4. The description does not need to explain parameters; it instead clarifies what the resulting statistics cover, which is the only relevant semantic information.

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 resource (directory) and states that it provides headline coverage: total programs, categories, networks, and verified counts. It does not use an explicit verb like 'returns' or 'lists,' but the meaning is clear and distinguishes it from sibling tools that fetch individual programs or list categories/networks.

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?

There is no guidance about when to choose this tool over siblings such as get_affiliate_program, list_categories, list_networks, or search_affiliate_programs. The aggregate nature of the stats is implied, but no explicit when-to-use or when-not-to-use instruction is given.

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 communicates a read-only listing behavior and the per-category count, but it does not mention ordering, whether counts are live or approximate, or any other restraints. Adequate for a simple zero-parameter list, though gaps remain.

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 action and resource, with no filler or redundant content. Every word contributes to understanding.

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 zero-parameter tool with no output schema, the description covers the essential behavior and return detail: categories with counts. It lacks explicit usage context or alternatives, but the low complexity makes this omission minor.

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 the schema is empty with 100% coverage, so there are no parameter semantics for the description to clarify. The baseline of 4 for zero-parameter tools 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 uses a specific verb ('List') and resource ('program categories'), and adds the count detail, making the operation clear. It is distinguishable from sibling tools like list_networks by the resource type, though it does not explicitly name alternatives.

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 verb and noun imply usage when a summary of program categories is needed, but there is no explicit guidance on when to choose this over sibling tools or any exclusion criteria. The usage is reasonably inferable but not stated.

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 behavioral disclosure burden. It clearly implies a read-only listing and specifies the output content, but it does not mention response shape, pagination, ordering, or whether all networks are included. This is adequate but not fully 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 a single, direct sentence that front-loads the action and resource. Every word contributes meaning, with no filler or 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 parameterless tool, the description gives enough context to select and invoke it: it lists networks and program counts. However, without an output schema, it leaves some ambiguity about response format and whether the list is paginated or 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 zero parameters, so the baseline is 4. The description does not need to explain parameter meanings since there are none to document.

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 ('List'), a clear resource ('affiliate networks'), and a distinguishing detail ('with a count of programs on each'). This separates it from siblings like get_affiliate_program (single program details) and list_categories (category listing).

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 makes the use case clear: when an agent needs an overview of affiliate networks and their program counts. It does not explicitly mention alternatives or exclusions, but the purpose is evident enough for appropriate selection among the given siblings.

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 behavioral disclosure burden. It adds value by explaining the 'MODELLED value per referral', ranking, and pagination, which are important non-obvious traits. It does not explicitly mention side-effect safety or rate limits, but 'search and filter' strongly implies a read-only directory operation.

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 compact, front-loaded with the core action, followed by concrete usage examples and the return behavior. Every sentence earns its place with no redundant or vague wording.

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 many optional filters and no output schema, the description covers purpose, common intents, ranking, and pagination. Minor gaps remain, such as not describing the per-item fields in the returned list and not spelling out how to discover allowed enum values, though the schema points to list_networks and list_categories.

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 9 parameters in detail. The description reinforces which filters matter most (category, network, recurring, minimum value) but does not add significant new semantic detail 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 searches and filters an affiliate program directory, listing concrete comparison dimensions (network, commission, cookie window, modelled value). It also names the exact user intents it serves, which distinguishes it from the sibling tools that retrieve a single program, stats, or enum lists.

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 use cases: best/highest-paying affiliate programs and filtering by category, network, recurring vs one-time, or minimum value. It does not explicitly say when not to use it or name alternatives like get_affiliate_program, but the stated use cases are clear enough for an agent to route to this tool.

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