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CiteLadder

by com.citeladder
OverviewDiscussionsAdmin

Read answer perception

read_perception
Read-onlyIdempotent

Read how AI answers portray the brand and competitors in the latest or a chosen run: net sentiment (-100 to +100) per brand, engine, topic and prompt, each with its coverage; praise and criticism by theme; how often answers recommend or advise against the brand; and the sources cited alongside criticism. The summary view returns all of that; the quotes view lists the verified quotes behind it, paged (50 by default, pass next_cursor). The response is always available: read perception_state, where pending, unavailable or no_mentions means there is no value, never zero. Always state the coverage. Sources are cited alongside criticism, never its cause. Use read_fact_checks for whether answers are factually right, and read_visibility_overview for how often the brand is mentioned at all.

Input Schema

NameRequiredDescriptionDefault
viewNosummary is sentiment and themes per brand; quotes lists the verified quotes, paged.summary
limitNoHow many rows to return; omit for the default page size.
themeNoWith view quotes: keep only quotes about this theme.
cohortNocore is the scored prompts (the default); comparison is the prompts that also name a competitor, reported separately; all is both.core
cursorNoThe next_cursor a previous page returned.
engineNoRead one engine only; omit for every engine.
entityNoWith view quotes: a brand exactly as the summary names it, to quote only it.
audit_idNoA run id (audit_id); omit for the latest run.
polarityNoWith view quotes: keep only praise or only criticism.
market_idNoA market id from get_project_business_context; omit for the default market. Markets are never pooled.
project_idYesA project id that list_projects returned.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed10 schema fields changed
    • addedInput schema / properties / audit_id / description
      Added value: +"A run id (audit_id); omit for the latest run."
    • addedInput schema / properties / cohort / description
      Added value: +"core is the scored prompts (the default); comparison is the prompts that also name a competitor, reported separately; all is both."
    • addedInput schema / properties / engine / description
      Added value: +"Read one engine only; omit for every engine."
    • addedInput schema / properties / entity / description
      Added value: +"With view quotes: a brand exactly as the summary names it, to quote only it."
    • addedInput schema / properties / limit / description
      Added value: +"How many rows to return; omit for the default page size."
    • addedInput schema / properties / market_id / description
      Added value: +"A market id from get_project_business_context; omit for the default market. Markets are never pooled."
    • addedInput schema / properties / polarity / description
      Added value: +"With view quotes: keep only praise or only criticism."
    • addedInput schema / properties / project_id / description
      Added value: +"A project id that list_projects returned."
    • addedInput schema / properties / theme / description
      Added value: +"With view quotes: keep only quotes about this theme."
    • addedInput schema / properties / view / description
      Added value: +"summary is sentiment and themes per brand; quotes lists the verified quotes, paged."
  2. Changed1 schema field changed
    • changedInput schema / properties / cohort / enum
      Previous value: -[
-  "core",
-  "comparison"
-]New value: +[
+  "core",
+  "comparison",
+  "all"
+]
  3. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations (readOnlyHint=true, idempotentHint=true) already cover the safety profile, yet the description adds substantial operational context: the response is always available, and pending/unavailable/no_mentions must never be read as zero; coverage must always be stated; and sources are cited alongside criticism but are never its cause. These are non-obvious behavioral traits an agent could easily misread, and they are disclosed clearly.

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 primary content is front-loaded in the first sentence, with view/paging semantics and the routing alternatives following. The prose is dense with several ideas per sentence and could be broken up, but every clause carries decision-relevant information, so little is wasted.

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 and 11 parameters, the description carries the full burden of describing returns, and it does: sentiment scale, per-dimension coverage, theme breakdowns, recommendation signal, source attribution, the two view shapes, and paging. Combined with 100% schema coverage, an agent has everything needed to call this 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 100%, so the schema already documents view, cohort, cursor, engine, entity, polarity, and market_id. The description still adds value beyond it by stating the default page size (50) and the next_cursor round-trip, which the schema only alludes to as 'the default page size.' That is a modest but real increment over the schema baseline.

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 ('Read how AI answers portray the brand and competitors') and enumerates the concrete outputs: net sentiment (-100 to +100) per brand/engine/topic/prompt with coverage, praise and criticism by theme, recommendation/advice rates, and cited sources. It also explicitly differentiates itself from read_fact_checks and read_visibility_overview, so an agent can distinguish it from siblings without opening a schema.

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

Usage Guidelines5/5

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

It names the two alternatives and the exact conditions that select them: 'Use read_fact_checks for whether answers are factually right, and read_visibility_overview for how often the brand is mentioned at all.' It also explains the summary-vs-quotes view split and the paging mechanism (50 default, pass next_cursor), leaving no routing ambiguity.

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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