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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint=false, and destructiveHint=false, so the safety profile is clear. The description adds the specific audit criteria (evidence, alternatives, missing context, confidence, falsifying checks), which is useful behavioral context, but it does not explain what happens after the check or how findings are reported.

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 filler. Every clause names a distinct audit criterion and 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?

The tool is a simple audit with no output schema, so the description should ideally explain what the audit returns (e.g., pass/fail, findings). It lists the criteria but omits return behavior and usage context, leaving a notable gap given the absence of an output schema.

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. It names the concepts behind three of the four parameters (evidenceExcerpts, alternativeExplanations, missingContext) and implies the claim, but it does not indicate that these are arrays, mention length limits, or fully describe the required claim 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 gives a specific verb ('Check whether') and a clear resource ('proposed interpretation'), and it enumerates the audit criteria. It does not differentiate from sibling tools (e.g., build_chat_analysis_brief, score_interaction_signals), so it stops short of a 5.

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 when-to-use guidance, no prerequisites, and no alternatives are mentioned. The agent must infer the context from the tool name alone.

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?

Annotations already declare readOnly, idempotent, and non-destructive, so safety is covered; the description adds the meaningful trait that no transcript is uploaded, reinforcing the privacy-preserving behavior. It still omits that a consent confirmation is required and whether any validation gates the call.

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?

A single dense sentence, front-loaded with the verb and the artifact, with the key constraint ('without uploading a transcript') placed at the end. No filler sentences, though the clause list is long enough to verge on run-on.

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 an 8-parameter tool with 0% schema coverage and no output schema, the description is thin. It never clarifies the required consentConfirmed flag or the risk-signaling of containsHighRiskData, which are central to the 'privacy-aware' framing in the title.

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 carries the burden. It explicitly names focusedQuestion, participantAliases, and knownGaps and hints at platform via 'WhatsApp or iMessage', but leaves relationshipContext, dateRange, consentConfirmed, and containsHighRiskData unexplained beyond vague references to 'privacy blockers'.

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?

States a specific verb ('Structure') and resource ('chat analysis brief') and enumerates the constituent parts (question, aliases, gaps, privacy blockers, evidence plan), so the agent knows exactly what gets produced. It does not explicitly contrast itself with siblings like audit_chat_claim or score_interaction_signals, but the purpose is 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?

The phrase 'without uploading a transcript' implies the intended usage context (privacy-preserving pre-analysis structuring) but there is no explicit when-to-use, when-not, or named alternative. The agent must infer that this precedes actual transcript analysis rather than replacing it.

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?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description modestly reinforces this with "without fetching external content" and adds the notion of "canonical" resources, but that partially restates the openWorldHint annotation rather than adding new 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?

A single front-loaded sentence with no filler. It is appropriately sized for a one-parameter read tool, though the trailing "without fetching external content" clause is the only slightly redundant part given the annotations.

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, safe, single-param read tool with annotations covering the safety profile and no output schema, the description is minimally adequate. It could say more about what distinguishes the resource categories to fully guide correct topic selection.

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 carry the parameter meaning. It enumerates workflow/example/relationship/safety, which corresponds to four of the five enum values, but it omits the "all" default and does not explain what each resource category contains.

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 pairs a clear verb ("Return") with a specific resource ("canonical What Brandon Thinks workflow, example, relationship, or safety resources") and states the scope constraint that no external fetching occurs. It is distinct from the sibling analysis/audit tools, though it doesn't explicitly contrast itself with them.

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?

There is no guidance on when to call this versus the sibling tools (build_chat_analysis_brief, score_interaction_signals, audit_chat_claim) or on prerequisites. The purpose implies a lookup use case but no explicit context or exclusions are 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?

Annotations already cover safety (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description adds a valuable behavioral scope statement: it is a deterministic calculator of shares/rates that explicitly performs no affection/intent/compatibility/personality judgment. It omits return format, but that is a modest gap against the covered safety profile.

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?

A single sentence that leads with the action and follows with the scope exclusion. There is no filler and every clause carries information.

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 a complex input (2-20 participant objects, seven required counts each) and no output schema, the description explains the conceptual purpose but not the per-field input expectations or the returned shares/rates. The exclusion clause is helpful, but the definition is not fully self-sufficient for correct 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?

Schema description coverage is 0% for the single participants parameter, a complex array of objects with seven required numeric counts each. The description hints at the input shape via 'user-supplied aggregate counts' but never enumerates or explains the required per-participant fields, so it only partially compensates for the schema's silence.

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 names a specific action (calculate) and resource (shares and rates from aggregate counts), and it fences off a whole class of functions by stating it does not score affection, intent, compatibility, or personality. This makes its purpose concrete and separates it from interpretive/scoring tools. It stops short of naming which sibling to use instead, so it is clear but not sibling-differentiated.

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 'from user-supplied aggregate counts' implies the caller must pre-aggregate, and the negative clause signals this is not the tool for qualitative scoring. However, no explicit when-to-use condition or named alternative (e.g., build_chat_analysis_brief) is given, so the guidance is only implied.

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