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

With no annotations provided, the description carries the burden of behavioral disclosure. It states that the tool returns various data points, implying a read-only operation, but doesn't mention failure cases, parameter precedence, or any side effects. Minimal transparency beyond the core lookup behavior.

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, information-dense sentence that front-loads the action and lists return categories without any redundancy or extraneous 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?

Since there is no output schema, the description compensates by enumerating return categories, which is helpful. It omits some edge-case behavior (e.g., when both slug and query are given), but the schema covers that. Overall, the description is sufficiently complete for a straightforward lookup 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?

Both parameters have full schema descriptions (100% coverage), so the baseline is 3. The description reinforces the slug-or-name dichotomy but adds no new semantic detail 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?

The description clearly specifies the tool's function: looking up a company on xtrust.info by slug or name. It also enumerates specific return data, distinguishing it from search_companies and post_review, though it doesn't explicitly contrast with siblings.

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 usage when an exact slug or company name is available, but it does not provide explicit guidance on when to prefer this tool over search_companies or when not to use it. No alternatives or exclusions are mentioned.

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 full responsibility. It discloses critical behaviors: requires OAuth 'write' scope, the review is labeled AI-agent-generated, shown in a separate section, and does not affect human trust rating. This goes well beyond a basic write operation description.

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 main purpose, and every sentence adds value. No fluff 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?

The description covers the purpose, auth requirement, and behavioral outcomes. It doesn't mention the response format or error scenarios, but since there is no output schema, these are not strictly required. The key details for correct invocation are present, making it nearly 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 description coverage is 100%, so the schema already fully explains the three parameters. The description adds no parameter-specific details, but that is unnecessary. It does add the OAuth prerequisite, but that is more behavioral context than parameter semantics. 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 verb and resource: 'Post a company review on behalf of the authenticated user.' It distinguishes itself from sibling tools (lookup_company, search_companies) which are read-oriented, whereas this is a write operation.

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: posting a review on behalf of the user. It mentions the prerequisite OAuth token with write scope. It doesn't explicitly name alternatives or exclusions, but the sibling tool names make the distinction 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 provided, the description carries the full burden of behavioral disclosure. It adds useful context by specifying prefix-based search, optional city filter in Ukrainian, and that it returns rating and link. However, it omits potential behaviors like pagination, result limits, error scenarios, or authentication requirements.

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, front-loaded sentence with no wasted words. It efficiently conveys the core action, optional filter, and return 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?

With only two parameters, full schema coverage, and no output schema, the description adequately covers the essentials: purpose, input semantics, and return value (matches with rating and link). It could add detail on result limits or ordering, but for a simple search tool, it is sufficiently 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 covers 100% of parameter descriptions, so baseline is 3. The description restates the schema semantics ('by name prefix', 'optionally filter by city') without adding new meaning. It clarifies that the query is a prefix rather than an exact match, but this was already implied in the schema description.

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 ('Search'), the resource ('companies on xtrust.info'), and the method ('by name prefix') with an optional filter ('by city'). It distinguishes itself from siblings by describing a search over multiple companies, contrasting with lookup_company's likely targeted retrieval.

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 basic context for what the tool does but offers no explicit guidance on when to use this tool versus the sibling tools (lookup_company, post_review). It doesn't mention alternatives or exclusions, leaving the decision to the agent's inference.

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