zero-core-verify

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

With no annotations provided, the description carries the full burden of disclosing behavior. It reveals that the tool returns specific aggregated metrics and that these are based on all past verifications, giving insight into its computational basis. However, it does not mention side effects, permissions, or potential limitations (e.g., whether it is read-only, latency, or behavior with no verifications).

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, front-loaded with the primary purpose, and lists the return metrics efficiently. Every sentence adds value, and there is no redundant or filler content.

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 (one parameter, no output schema), the description provides sufficient context by defining the input service_id, the aggregation basis (past verifications), and the returned metrics. It lacks details about response format (e.g., are rates decimals or percentages?) and edge cases, but these are not critical for basic 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 schema already provides full coverage for service_id, including a description and example. The tool description does not add additional meaning about the parameter beyond restating that it queries a service. Since schema coverage is 100%, the baseline of 3 is appropriate; the description adds no extra semantic value.

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's function: 'Get aggregated quality scores for a service based on all past verifications.' It uses a specific verb (Get) and resource (quality scores for a service), and it enumerates the outputs (completeness, accuracy, pass rate, etc.), which distinguishes it from sibling tools like verify_outcome or report_outcome that deal with individual verifications.

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 context: it aggregates past verifications, suggesting it is for reviewing overall service quality rather than individual outcomes. However, it does not explicitly state when to use this tool versus alternatives or mention any exclusions, such as 'use verify_outcome for individual checks'.

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 must disclose behavior. It implies a read-only check via the verb 'Check', but does not explicitly state safety, permissions, or lack of side effects. It is minimally transparent but lacks explicit behavioral context.

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, focused sentence that conveys the essential purpose without fluff. It is front-loaded with the action and resource, earning a top score for efficiency.

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 health-check tool with no parameters and no output schema, the description covers the main aspects. However, it does not explain what the response contains (e.g., status object, error behavior), leaving a gap in the agent's understanding of what to expect.

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 confirms this with an empty object. The baseline is 4 because no parameter explanation is needed; the description adds no parameter info, which 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 uses a specific verb 'Check' with a clear resource ('Harvey Verify server') and details what is checked (status, uptime, payment network configuration). This clearly distinguishes it from sibling tools like get_service_quality or report_outcome.

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 no guidance on when to use this tool versus alternatives. It does not mention explicit use cases, prerequisites, or exclusions, leaving the agent without context for tool selection.

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 states that the tool lists all tools with pricing and input requirements, which is a clear, read-only operation. It doesn't disclose whether auth is needed or if there is any cost, but for a simple list tool this 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?

The description is a single sentence that is concise, front-loaded, and contains no filler. Every word contributes meaning, covering what the tool does and when to use it.

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, parameterless discovery tool, the description is fully sufficient. It explains the return contents (pricing and input requirements) and the use case, and no output schema is present, so the description does not need to explain return values beyond what it already states.

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 description coverage is 100%. Per the rubric, a baseline of 4 is appropriate when there are no parameters, as the schema already provides complete 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 uses a specific verb ('List'), names the resource ('all available Harvey Verify tools'), and specifies the content ('pricing and input requirements'). This clearly distinguishes it from sibling tools like 'get_service_quality' or 'verify_outcome', which are domain operations rather than a discovery/meta tool.

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 phrase 'Use this for discovery' provides clear context for when the tool should be invoked. However, it does not mention when not to use it or name alternatives, so it lacks explicit exclusions or alternative references.

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 full burden. It discloses key behaviors: logs to the quality database, does no LLM analysis, and is cheap. However, it does not mention potential side effects, error handling, or whether the operation is idempotent.

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 primary action and followed by a concise differentiation. No wasted words; every sentence 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 simple logging tool with 4 parameters and no output schema, the description covers what, when, and how it differs from an alternative. It lacks explicit return value info, but for a record-only tool this is not critical. Context 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 description coverage is 100% with each parameter having a clear description, so the baseline is 3. The tool description adds no additional parameter context 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?

Description clearly states the tool records a simple pass/fail outcome report for a service call, with specific verb ('Record') and resource ('outcome report'). It differentiates from sibling verify_outcome by noting it is cheaper and does no LLM analysis.

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?

Explicitly says 'Cheaper alternative to verify_outcome when you only need to record success/failure', providing clear when-to-use guidance and naming the alternative. Also implies not to use when LLM analysis is needed.

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 empty annotations, the description carries the transparency burden. It discloses the LLM-as-judge method and a list of returned metrics, but does not mention side effects, permissions, or read-only 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 two sentences, front-loads the core purpose, and each phrase provides useful information about method and outputs.

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 purpose, method, and returned fields, which is important given no output schema. However, it lacks guidance on when to choose it over sibling tools and does not address side effects or prerequisites, making it slightly incomplete for a 5-param 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?

All 5 parameters have schema descriptions (100% coverage), so the baseline is 3. The description mentions metrics like SLA adherence and format compliance, tying to expected_schema and sla_requirements, but adds no additional parameter-level guidance.

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 action: post-transaction verification using LLM-as-judge, checking if a service delivered what was promised. This clearly distinguishes it from siblings like report_outcome or get_service_quality.

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 implied usage context ('post-transaction') but does not explicitly state when to use this tool over siblings, nor any alternatives or exclusions.

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