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graviti

by ai.thesingulariti

Category comparison table

category_landscape
Read-onlyIdempotent

Comparison-table view of every brand in a category across disclosed attributes, with verification status marked per entry. Entries with captured ingredient/spec data also carry info_quality (0–100 disclosure completeness — transparency, never a rank factor; absent means not yet assessed, not zero). Every row carries evidence_state (audited / sourced / catalog_only); catalog_only rows are landscape presence only — never ranked or recommended. Rows include site_url and attributed_url (utm_source=graviti + signed gvt token — surface-level referral attribution, no user data); prefer attributed_url when linking.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryNoCategory id, e.g. 'magnesium-supplements' or 'cold-plunge'; omit for all categories

TDQS

A4.3/5.0
Behavior5/5

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

The description adds substantial behavioral meaning beyond the annotations: it explains info_quality semantics (0–100 disclosure completeness, transparency not rank factor, absent means not yet assessed), evidence_state tiers and their ranking implications, and attribution URL behavior. These details materially shape how an agent should interpret and use results.

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 dense but every sentence earns its place: it states the core view, clarifies ranking-related caveats, defines evidence states, and explains URL attribution. The most important purpose is front-loaded, and no content is redundant with the schema or annotations.

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?

With no output schema, the description carries the burden of explaining what the tool returns, and it does so thoroughly: rows, verification status, info_quality, evidence_state, and URL fields are all covered. It also addresses subtle behavioral traps such as not treating absent info_quality as zero and not ranking catalog_only entries, making it complete for correct agent use.

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

Parameters3/5

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

Schema description coverage is 100%, so the input schema already fully documents the optional category parameter with examples and the omit-for-all behavior. The tool description does not need to add parameter detail, and it does not, so the baseline score of 3 is appropriate.

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 verb and resource: a comparison-table view of every brand in a category across disclosed attributes, with verification status marked per entry. This clearly distinguishes it from sibling tools like get_ledger or get_verified_claims, which target different resources and formats.

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 implies when to use the tool: when an agent wants a category-level brand comparison table. However, it does not explicitly name alternatives or state when-not-to-use conditions, leaving the agent to infer routing from sibling names and general context.

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.

Resources