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CiteLadder

by com.citeladder
OverviewDiscussionsAdmin

Read ads in AI answers

read_ai_ads
Read-onlyIdempotent

Paid ads shown in ChatGPT Search answers for the latest or a chosen run: ad presence rate ("N of M ChatGPT answers"), advertisers with share and first/last seen, the ad share and best rank of the brand itself, text creatives (paged) and prompts that surface ads with competitor ads beside organic brand mentions. Only ChatGPT Search shows ads; other engines are not_applicable, never zero. Ads are paid placements: report them separately, never as citations, sources or a cause of visibility.

Input Schema

NameRequiredDescriptionDefault
limitNoHow many rows to return; omit for the default page size.
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.
audit_idNoA run id (audit_id); omit for the latest run.
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. Changed6 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 / 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 / project_id / description
      Added value: +"A project id that list_projects returned."
  2. Changed1 schema field changed
    • changedInput schema / properties / cohort / enum
      Previous value: -[
-  "core",
-  "comparison"
-]New value: +[
+  "core",
+  "comparison",
+  "all"
+]
  3. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already establish read-only, idempotent, non-destructive behavior, yet the description adds substantial domain context beyond them: the not_applicable-vs-zero semantics for non-ChatGPT engines, the paged nature of creatives, and the reporting rule that ads are paid placements to be reported separately, never as citations or a cause of visibility.

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?

Front-loads the returned contents before the caveats, and the key reporting constraint is placed last for emphasis. The middle enumeration is dense but each clause maps to a distinct returned element, 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 a non-trivial seven-parameter surface, the description supplies the return contents, engine applicability, and reporting rules an agent needs. Nothing required to call or interpret the tool correctly is missing.

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 coverage is 100%, so all seven parameters are already documented, including the cohort enum and engine list. The description adds domain framing (runs, engines, paged creatives mapping to cursor/limit) but no syntax or format detail the schema lacks, so the baseline 3 applies.

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?

States a specific verb (read) and resource (paid ads shown in AI answers) and then enumerates the concrete contents — presence rate, advertisers with share and first/last seen, brand ad share/rank, text creatives, prompts surfacing ads. This clearly distinguishes it from siblings like read_ai_shelf and read_ai_referrals that also concern AI visibility.

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?

Explains scoping via 'latest or a chosen run' and the crucial engine rule: only ChatGPT Search shows ads while other engines are not_applicable, never zero. This tells the agent how to interpret results, though it does not explicitly name an alternative sibling tool to use instead for other visibility data.

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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