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Report an attributed conversion (demo stub)

report_conversion

Appends one attributed-transaction event to a log — the demo stub of the pay-on-real-acquisition loop. It moves no money, calls no external service, and never deletes or overwrites anything; the response states where (and whether) the event was persisted on this transport. Payment only ever flows on real transactions, never on impressions or rank. Optionally pass the gvt attribution token from the recommendation link that led to the purchase: it is verified against the published Ed25519 key and recorded with the event as verifiable provenance of the originating recommendation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
gvtNoOptional: the gvt token from the attributed_url of the originating recommendation (gvt1.<surface>.<category>.<brand>.<date>.<sig>) — binds this conversion to a verifiable recommendation surface
brand_idYes
agent_platformYese.g. 'claude', 'chatgpt', 'perplexity'
conversation_idYesOpaque id for the agent conversation that led to purchase
order_value_usdYes

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the annotations, the description discloses critical non-obvious behaviors: it moves no money, calls no external service, never deletes or overwrites anything, and the response states where and whether the event was persisted. This substantially exceeds the annotation signals and gives an agent a safe, accurate mental model of the operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a focused three-sentence paragraph with the core behavior front-loaded. Every sentence earns its place: what it does, what it does not do, and how to use the optional attribution token. There is no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity, no output schema, and the presence of a safety-relevant annotation set, the description is complete. It clarifies persistence behavior, response intent, financial safety, and the optional verification flow, which is more than enough for an agent to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 60%, and the description adds real meaning for the gvt parameter by explaining its provenance and Ed25519 verification. It does not deeply elaborate brand_id or order_value_usd, but those are reasonably self-descriptive, and the gvt guidance goes well beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Appends one attributed-transaction event to a log.' It clearly identifies this as the conversion-reporting tool and differentiates itself from the get_* sibling tools, which are read-only and have no mutation role.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description conveys that this tool is for reporting real attributed conversions, explicitly stating 'Payment only ever flows on real transactions, never on impressions or rank.' It does not name alternative tools, but the contrast with all-getter siblings makes the intended use clear enough without explicit exclusions.

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

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TDQS

A4.2/5.0
Disambiguation4/5

Each tool targets a distinct resource: category comparisons, verified claims, gap analysis, intent matching, conversion logging, accountability events, and the index ledger. The only mild ambiguity is between category_landscape and match_intent, or between get_accountability_log and get_ledger, but the descriptions draw clear boundaries.

Naming Consistency4/5

Most read operations follow a clean get_ pattern, and the action tools use verb_noun naming. category_landscape deviates as a bare noun phrase, which is the main inconsistency, but overall the names remain readable and predictable.

Tool Count5/5

Seven tools is well-scoped for this domain: consumers can discover brands, inspect evidence, evaluate gaps, audit integrity, and record conversions without redundant operations or missing categories.

Completeness4/5

The set covers the core workflows: discovery, verification lookup, gap analysis, audit/log integrity, and conversion attribution. There are minor gaps—no direct tool to create or update claims—but agents can accomplish the intended consumer-facing tasks without dead ends.