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

With no annotations, the description bears the full behavioral disclosure burden. It reveals the return limit ('Returns up to 3 examples') and adds a usage constraint ('never copy sentences from them'). The verb 'Retrieve' sufficiently implies a read-only operation for this kind of tool. It could optionally disclose more about the output shape, but that is already structurally represented by an output schema.

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 contain the entire message with no filler. The tool's value is front-loaded, followed by a practical example, a return limit, and a usage constraint. Every clause earns its 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?

For a single-parameter retrieval tool with an output schema available, the description covers what is returned, how to query, and a practical guardrail. The only omission is a note about the default parameter, but the schema already declares a default. The agent can select and invoke the tool with confidence.

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 on the only parameter, `query`, so the description must compensate. It does: the parameter can be used to search by topic, channel, or type, and an example value is given. It does not describe what empty input does, though schema's default value covers that reasonable.

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 and resource: 'Retrieve past posts with their outcomes'. It also adds the intended use, 'tone calibration', which makes the tool's function concrete. It is clearly distinguishable from siblings like search_guidelines and list_visual_templates because neither of those fetches example posts.

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 tells the agent that querying can be by topic, channel, or type, with a concrete example ('linkedin annuncio'). It does not explicitly contrast with search_guidelines or list_visual_templates, but the purpose of tone calibration and the example query provide enough context. A score of 5 would require explicit when-to-use versus the 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 providing read-only or side-effect hints, the description carries the behavioral burden. It communicates that the tool lists an official catalog, returns usage rules with T# IDs, and behaves differently based on the query parameter. It does not go further to describe matching behavior, ordering, or whether any 'rules' are embedded in the returned data, but the core read-oriented behavior is reasonably clear.

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, front-loads the main purpose, and uses an example to clarify parameter usage. There is no filler or repetition of schema fields, and every phrase 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?

For a simple list-with-optional-filter tool, the description covers the tool's purpose, the optional query, and the empty-query behavior. The presence of an output schema compensates for not describing the return structure. Minor ambiguity remains about what 'content type' means and how T# IDs are used, but as a whole it is largely 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?

Schema description coverage is 0%, so the description must compensate, and it does. The 'query' parameter is given semantics: it optionally filters by content type, with the example 'annuncio corso', and empty returns the full catalog. This is meaningful beyond the raw string schema. It could be slightly stronger, but it sufficiently explains the one parameter.

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 a specific action, 'List the official visual template catalog', with a resource and an explicit mention of what makes these templates usable: the T# IDs. It does not explicitly contrast itself with the sibling tools search_guidelines or get_post_examples, so it stops short of full differentiation, but the verb and resource are 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?

It explains when the query parameter is useful and that an empty query returns the full catalog, which provides practical guidance for using the tool. However, it does not explicitly state when to prefer this tool over sibling tools such as search_guidelines or get_post_examples, so the usage context remains partially implied.

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 indicating behavior safely. It does disclose that the tool returns 'matching rule blocks with their IDs (R#)', which explains the shape of the output. However it omits details such as whether it is read-only, how many results are returned, or any limitations on query syntax.

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 only: the first states purpose, the second gives return format and a clear example. Every sentence serves a direct purpose with no fluff. It is appropriately front-loaded with the core action.

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 search tool with an output schema already defined, the description is nearly complete. It explains the input style and output format for typical searches. It still lacks usage guidance relative to siblings, which slightly reduces 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?

Parameter schema coverage is 0% since the query string has no description. The description compensates by giving a concrete example ('tone course post linkedin'), which clarifies that the query is free-form text and likely case-insensitive phrase style. This is meaningful added meaning beyond the bare 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 a specific action ('Search') and a precise resource ('company brand and editorial rules'). It distinguishes this as a search tool from the sibling tools (list_visual_templates, get_post_examples), though it could be more explicit about exactly how it differs.

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 does not say when to use this tool rather than the siblings. It gives no context, precedence, or exclusion. The example query implies usage, but an agent is given no guidance to choose this over the alternatives.

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