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graviti

by ai.thesingulariti

Public accountability log

get_accountability_log
Read-onlyIdempotent

Public, machine-readable log of every seal lifecycle change: flags, degradations, revocations, and restorations, with dates and reasons — plus confirmation-loop events (event: confirmation_request / confirmation_resolved), where the audit asked a brand to confirm a claim it couldn't re-verify: a visible ask, never a penalty. Also logged: claim_removed (a claim struck from a record, reason public), gap_report_delivered (a paid-tier report delivered to a brand, payment status disclosed — findings and rank never affected), and roster events (brand_added with lane/source, brand_removed, brand_declined, brand_skipped — every future change to who is in the index lands here per brand, reason public). The seal can be lost — a pulled seal is the loudest proof it means something. Payment can fast-track a re-evaluation; it can never change what it finds.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.3/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint:false, and the description goes well beyond them. It discloses that the log is public and machine-readable, that confirmation asks are visible but 'never a penalty,' that payment status is disclosed but 'findings and rank never affected,' and that roster changes are permanently recorded. These are meaningful behavioral guarantees an agent needs to understand trust and side-effect semantics.

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

Conciseness4/5

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

The description is front-loaded with the central concept ('Public, machine-readable log of every seal lifecycle change') and then groups related event types. It is longer than strictly necessary and includes one mostly rhetorical sentence ('a pulled seal is the loudest proof it means something'), but most clauses carry useful operational meaning.

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

Completeness4/5

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

Given no parameters and no output schema, the description ably covers what the log contains and its behavioral guarantees. It does not specify pagination or output shape, but for an unfiltered public log with rich annotations, the description is sufficiently complete 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?

The tool has zero parameters and schema coverage is 100%, so there is nothing for the description to add about parameters. The description indirectly affirms the unfiltered nature by saying 'every seal lifecycle change' and 'every future change,' which supports the baseline of 4.

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 states a specific resource ('Public, machine-readable log') and enumerates its exact scope: seal lifecycle changes, confirmation-loop events, claim removals, gap report deliveries, and roster events. This makes the tool's purpose unambiguous and clearly distinguishes it from sibling getters by content coverage.

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

Usage Guidelines3/5

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

The description implicitly tells an agent when to use this tool by listing what it returns, but it never explicitly contrasts it with get_ledger, get_verified_claims, or get_gap_report. There are no when-to-use/when-not-to-use guidelines or named alternatives, so the agent must infer selection criteria from content rather than receive direct guidance.

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.

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