Quesen

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

No annotations are provided, and the description only says 'Liveness probe.' It does not disclose any behavioral traits such as read-only nature, potential side effects, authorization requirements, or rate limits. The minimal information leaves much to be inferred.

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 extremely concise with two short sentences. It is front-loaded with the core purpose 'Liveness probe' and immediately specifies the return values. Every word is useful.

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 no output schema, the description provides basic information about what is returned ('engine_version and transport'), but lacks details on success conditions, error responses, or typical use cases. For a simple health endpoint, it is minimally adequate.

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 no parameters, and schema coverage is 100% trivially. Per the guidelines, baseline is 4 for zero parameters. The description does not need to add parameter detail.

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 'Liveness probe' and specifies the returned fields 'engine_version and transport'. This distinguishes it from sibling tools like quesen.report, quesen.simulate, etc., which have different purposes.

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 explicitly state when to use or not use this tool. However, the purpose 'liveness probe' implies its usage for checking service health, and sibling names suggest distinct operations, making the context fairly clear.

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 carry the full behavioral burden. It mentions deterministic aggregation and correlation but does not disclose whether the tool modifies state, requires authentication, has rate limits, idempotency guarantees, or error handling. For a write operation, this is a significant 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?

The description is two sentences long with no extraneous information. It efficiently conveys the key purpose and inputs without unnecessary elaboration.

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 8 parameters (2 required, 6 optional), no output schema, and no annotations, the description falls short. It does not explain the return value, error conditions, or the meaning of the optional metadata fields. An agent would need to guess the usage of several parameters.

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% coverage, meaning no parameter descriptions are provided. The description only names request_id and outcome with the outcome enum values. Optional parameters like notes, venue, signal_hash, realized_pnl, elapsed_seconds, and client_request_id are not explained, leaving the agent without critical context for correct usage.

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 purpose: accepting post-decision outcome feedback with request_id and outcome (with specific enum values), and optional metadata. It also explains the aggregation use for correlation analysis. This distinguishes it from siblings like simulate or validate.

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 after a decision is made (post-decision outcome feedback) and lists required inputs. However, it does not explicitly state when not to use this tool or contrast it with alternatives, though sibling tools are distinct enough that confusion is unlikely.

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?

The description implies a non-destructive operation (simulation) and mentions 'not charged against key,' indicating no cost impact. However, without annotations, full behavioral transparency is lacking. It does not explicitly state whether the tool is read-only or whether it modifies any state. For a simulation, it is likely safe, but the description could be more explicit.

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 brief (two short sentences) with no extra fluff. The first sentence 'Counterfactual scoring.' is minimal, but the second sentence adds essential context. The structure is efficient, though the first sentence could be integrated for better flow. Overall, it is concise without significant waste.

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 has no output schema and 6 parameters, the description is incomplete. It explains the core logic (counterfactual comparison) but does not describe the return value or example output. Many parameters are undocumented, and there is no mention of required input fields. For a simulation tool, users would need to know what the output format is to interpret results.

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 coverage is 0%, so the description must compensate. It mentions only two parameters (weights_override and thresholds_override) out of six total. The other four parameters (domain_age_days, engagement_ratio, scam_keyword_count, client_request_id) are not described at all. While the overrides are central to the tool's purpose, the lack of documentation for input fields limits usability.

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 'Counterfactual scoring' and specifies that it compares a baseline decision against a simulated one using overrides. The verb 'simulate' and resource 'decision' are specific, and the mention of weights/thresholds overrides gives clear context. However, it does not explicitly differentiate from sibling tools, though the tool name 'simulate' naturally distinguishes it.

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 includes 'Free sales asset — not charged against key,' which provides cost-related guidance. However, it offers no explicit when-to-use or when-not-to-use advice, nor does it compare to alternatives like quesen.report or quesen.validate. The context signal 'sibling tools' suggests these are distinct, but no guidance 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 full burden. It discloses determinism, replayability (engine_version, weights, thresholds), and output format, but does not address side effects, authentication needs, rate limits, or safety profile (e.g., whether it mutates state). This is adequate but incomplete for a tool with zero annotations.

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 with no wasted words. The first sentence clearly states purpose and output; the second adds essential behavioral context (deterministic, replayable). Every sentence earns its place, making it easy for an agent to parse quickly.

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?

Despite no output schema, the description fully explains return values (PROCEED/REVIEW/SKIP, risk_score, confidence, conflict triggers, engine_version, weights, thresholds). Given 4 optional parameters and high schema coverage, the description is complete enough for an agent to understand invocation and results.

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 coverage is 100% with all parameters described in the schema. The description adds no parameter-specific details beyond what the schema provides, which aligns with the baseline of 3. It does, however, describe output fields, which aids understanding but does not elevate parameter semantics.

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 performs 'deterministic pre-decision risk validation for autonomous agents' and specifies exact outputs (PROCEED/REVIEW/SKIP, risk_score, confidence, conflict triggers). It distinguishes itself from siblings by being a validation tool while siblings like quesen.simulate and quesen.health serve different purposes.

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 says it is for 'pre-decision risk validation' and emphasizes determinism ('Same input -> same output'), which informs when to use it. However, it does not explicitly state when not to use it or compare to siblings like quesen.simulate, missing some guidance on alternatives.

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 provided. The description mentions 'weights, thresholds, versions' but does not disclose read-only or mutability, side effects, or output format. For a configuration tool, this is insufficient.

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?

Extremely concise (single phrase). No wasted words, but could be more descriptive. Acceptable for zero-parameter tool.

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?

No output schema, and description only lists three data types without structure or usage context. For a zero-parameter tool with multiple siblings, more completeness is needed.

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?

No parameters exist (schema coverage 100%), so baseline is 4. The description adds meaning by indicating the tool returns configuration items, compensating for the empty 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 is a noun phrase ('Full engine configuration') without a verb, leaving ambiguity whether the tool retrieves, modifies, or both. It lists data types but not the action.

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?

No guidance on when to use this tool vs siblings (health, report, simulate, validate). The context does not explain how 'version' differs from the others.

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