WIA360: AI session probe and match

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

Annotations already declare readOnly/idempotent/non-destructive/closed-world, but the description adds genuine behavioral context beyond them: the paid gate changes the returned payload (teaser counts vs score/grade/heatmap/priming prompts) and identifies where human axis values originate. It does not cover failure modes or what happens with mismatched refs.

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?

Purpose is front-loaded and every clause carries information (dependency, value source, paid gate). Sentence two is long and em-dash-heavy, but no sentence is filler, so it stays near-optimal for the tool's complexity.

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 no output schema, the description admirably discloses the two return shapes (free teaser counts vs full grade/heatmap/priming), the required dependency, and the paid condition. It omits error handling and any notion of stale or missing session data, leaving a small gap for a 5-required-parameter 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?

Schema coverage is 83%, so each axis parameter and paid are already documented in the schema. The description restates the four axis values and their WIA360 source, largely duplicating the schema's own '(1-5 average, from WIA360 result)' notes; it adds little new per-parameter meaning, so baseline 3 holds.

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 and resource ('Compute the compatibility match') and grounds it in the AI session profile built via score_session, so the agent knows exactly what artifact it produces. It clearly names the sibling score_session as a data dependency but never mentions run_probe, leaving one sibling relationship undifferentiated.

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 a clear prerequisite (a session profile already built via score_session under the same session_ref) and an explicit condition for the paid flag ('only when the human has an active paid WIA360 profile'). It stops short of stating when to call get_match versus score_session/run_probe in a workflow, so no explicit exclus...(truncated).

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 declare readOnlyHint=false and idempotentHint=false, and the description is consistent with a state-advancing call. It goes well beyond them by disclosing the 6-probe exhaustion signal, random ordering with de-duplication of finalized probes, the two-turn P4 delivery sequence, and the requirement to pass the target's raw reply to score_session.

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?

Dense but front-loaded: the core purpose and session_ref reuse rule come first, followed by delivery mechanics. Every clause carries load, though the long parenthetical and the P4/lang sentences make it a single heavy paragraph that could be broken up.

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 no output schema, the description compensates by describing the return value ({done:true} after 6 probes) and the mission payload (wrapped_mission, turn2_wrapped_mission). Nothing an agent needs to invoke and correctly consume this tool appears to be missing.

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%, so the baseline is 3, but the description adds real operational meaning: session_ref is caller-generated and must be stable across the run, and lang must match the language the target AI session actually converses in (not the caller's language) and be passed identically to score_session.

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 and resource: 'Get the next WIA360 session-probe mission for a given session_ref'. It immediately distinguishes itself from the siblings by naming score_session and get_match as the downstream steps in the same workflow.

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 when to call it (first time: generate a session_ref yourself; thereafter reuse the same one for run_probe/score_session/get_match), how missions are ordered (random, excluding already-finalized probes), and what terminates the loop ({done:true} after all 6). It also names the alternative sibling to use next for scoring.

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 declare readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false. The description adds important context: scoring uses a language-specific keyword dictionary and silently misfires if lang doesn't match. It also describes the retry workflow and 'invalid'/'omitted_pending' verdicts. That's valuable beyond annotations, though it doesn't mention authentication or rate limits.

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?

Front-loads the core action, then the retry flow, then the critical lang warning. Well-structured and information-dense. Slightly long but every sentence earns its place except perhaps the retry detail which could be condensed.

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 and 50% schema coverage, the description provides the key operational rules: verdict handling, retry logic, and lang matching. It omits details about what the scoring returns (verdict values) and the meaning of probe/variant, but for a scoring submission tool the critical workflow is covered. A little more on return values would make it 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 50%. The description explains lang (must match run_probe), retry_of (attempt_id from prior invalid/omitted_pending result), and response_text (raw reply, for P4 the TURN2 reply only). However, it does not explain probe, variant, or session_ref semantics, and those lack schema descriptions. Baseline 3 is appropriate given partial coverage and clear additions for some params.

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 (Submit) and resource (target AI session's raw reply text to a probe mission for rule-based scoring). Clearly distinguishes from run_probe (which produces the mission) and get_match. An agent can tell what this does without opening the schema.

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 to retry once via run_probe with the same session_ref, pass attempt_id as retry_of, and after a second failure move on. Also includes a critical prerequisite: lang must match the lang passed to the run_probe call that produced the mission. This is unusually thorough usage guidance.

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