AOPX

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 does reveal that provider identities are exposed only through public aliases, which is a meaningful privacy trait. However, it does not mention whether the operation is read-only, whether authentication is required, or any rate limits. For a simple read operation, this is partial but not comprehensive transparency.

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 long with the purpose front-loaded in the first sentence. The second sentence adds a concise privacy note. There is no redundant or vague language; every word contributes 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?

The tool is simple with one optional parameter and an output schema. The description covers the main purpose and a key privacy detail. It omits any mention of ordering or pagination, but given the existence of an output schema and the simplicity of the tool, this is not a critical gap. The description is nearly complete for the tool's complexity.

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 description makes no reference to the 'limit' parameter. Since schema description coverage is 0%, the description should have compensated by explaining the parameter's meaning, but it does not. The agent is left with only the schema's bare type and default, which is insufficient given the lack of any parameter documentation.

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 ('Return') and a precise resource ('latest reported production outcomes for AOPX'), which clearly distinguishes it from siblings like public_stats and report_outcome. It also adds a distinctive note about provider aliases, further clarifying what the tool does.

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 offers no explicit guidance on when to use this tool versus alternatives. It does not mention conditions, exclusions, or reference any sibling tool, leaving the agent to infer the appropriate context solely from the purpose statement.

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?

There are no annotations, so the description carries the behavioral burden. It adds meaningful context by specifying that LAB benchmarks, tests, shadow runs, and replay events are excluded, making clear the tool returns only production aggregate data. Given the output schema exists, the lack of return-format details 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?

Two concise sentences with the main purpose front-loaded and the data-scope exclusions clearly stated. No filler or redundant phrasing.

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?

This is a parameterless, read-only statistics tool with an output schema available, so the description only needs to convey what data is included and excluded, which it does. The exclusion sentence adds essential scope context, making the definition complete for successful invocation.

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 input schema is empty with 100% coverage. There is nothing for the description to add regarding parameter meanings, so the baseline 4 for no-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 ('Return') and identifies the exact resource: aggregate AOPX production consultation/outcome statistics. It clearly distinguishes itself from siblings like public_outcomes and report_outcome by emphasizing aggregate stats rather than individual outcomes or reporting actions.

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 by saying it returns production statistics and excluding non-production data, but it does not explicitly mention alternatives or provide when-not-to-use guidance. An agent can infer usage context, but the routing is not explicit.

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 explicitly states 'AOPX only recommends; it does not execute or pay the provider', which is a critical non-execution trait that prevents misuse. It also discloses the necessity of a follow-up call to feed future recommendations, giving the agent a clear sense of the tool's side effects and lifecycle. While it doesn't cover permissions or rate limits, the core non-mutating behavior is clearly surfaced.

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 the primary purpose, then states the key behavioral caveat, and finishes with the required follow-up. Every sentence adds essential information with no redundancy, making it 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?

For a tool with 4 parameters and no annotations, the description covers the core purpose, modes, and a critical post-execution step, which is useful. However, it fails to describe the purpose of the three optional parameters, and while an output schema exists, it does not mention any return details other than the recommendation_id indirectly. This leaves the agent guessing about parameter meanings and the structure of the recommendation result.

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 for unexplained parameters. It does explain the required 'mode' enum (ECONOMY and CRITICAL), but it completely omits the optional parameters client_id, max_cost_eur, and max_latency_ms, leaving their semantics unknown to the agent. This is a significant gap for a moderately complex tool.

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 'returns a provider recommendation' for a specific 'SEARCH category', which is a concrete verb+resource combination. It also distinguishes itself from sibling tools like report_outcome, public_outcomes, and public_stats, which are about outcome reporting or statistics, not recommendation generation.

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 on when to use the tool (to obtain a provider recommendation in ECONOMY or CRITICAL mode) and gives an explicit follow-up instruction to call report_outcome with the recommendation_id. It does not explicitly state when not to use it or name alternatives, but the workflow directive is strong enough to guide correct usage.

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 present, so the description carries the behavioral disclosure burden. It adds key operational constraints not visible in the schema: a recommendation accepts only one outcome, and attempts must match the recommended primary/fallback path. It does not detail side effects, authentication, or failure semantics, but the core write-once behavior is disclosed.

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?

Three sentences with no filler, front-loaded with purpose and call context. The constraints (id existence, single outcome, path match) are packed into the final sentence without 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 two-parameter tool with an output schema, the description provides the essential usage context: when to call, what to validate, and what shape the attempts must take. It misses an explicit statement about whether the report is a permanent write and any error/conflict behavior, but the information needed to invoke it correctly is largely 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?

Schema description coverage is 0%, so the description must add parameter meaning. It gives useful constraints for recommendation_id (must exist, one outcome only) and for attempts (must match the recommended path), but it does not explain the individual ProviderAttempt fields such as cost_eur, error_type, latency_ms, or quality_score. This is an adequate but incomplete compensation.

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?

Description opens with a specific verb and resource: reporting the real production result of a previous AOPX recommendation. It also orients the agent by tying the call to recommend_provider's returned route, which separates it from the read-only siblings public_outcomes and public_stats.

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?

Gives an explicit trigger: call after executing the provider path returned by recommend_provider. It also sets preconditions (recommendation_id must exist, single outcome per recommendation, attempts must match the path). It does not enumerate when-not-to-use cases or alternative tools, but the context is clear.

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