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CiteLadder

by com.citeladder
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Read a research dataset

read_search_dataset
Read-onlyIdempotent

Read the rows of one saved keyword or backlink research dataset, after read_search_intelligence has named it. Use it for keyword volumes, rankings, competitor keywords or referring domains; use read_search_intelligence to choose the dataset and read_query_evidence for the project's own Search Console queries. It returns the dataset (kind, target, market, saved row count) and a page of rows (50 by default; pass next_cursor for more, with the same sort), each with a citeladder:// id for fetch. Columns depend on the dataset kind. Referring-domain and page totals are provider aggregates, not individual links: never report them as a link list. Rows are as saved at research time and are never refreshed by this read.

Input Schema

NameRequiredDescriptionDefault
sortNoThe column to order by, which must exist for the dataset kind: one of backlinks, dataforseo_rank, difficulty, domain, etv, id, intent, keyword, owned_rank_group, rank_group, referring_main_domains, search_volume, url, backlinks_spam_score, broken_backlinks, broken_pages, cpc, organic_keywords, rank_absolute, referring_domains, referring_pages. Omit to order by row id. Rows with no value sort last.
limitNoHow many rows to return; omit for the default page size.
cursorNoThe next_cursor a previous page returned.
directionNoasc (the default) or desc; desc with a volume or rank column puts the largest first.asc
dataset_idYesA dataset id from read_search_intelligence; a dataset of another project is refused.
project_idYesA project id that list_projects returned.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed6 schema fields changed
    • addedInput schema / properties / dataset_id / description
      Added value: +"A dataset id from read_search_intelligence; a dataset of another project is refused."
    • addedInput schema / properties / direction / description
      Added value: +"asc (the default) or desc; desc with a volume or rank column puts the largest first."
    • addedInput schema / properties / limit / description
      Added value: +"How many rows to return; omit for the default page size."
    • addedInput schema / properties / project_id / description
      Added value: +"A project id that list_projects returned."
    • addedInput schema / properties / sort / description
      Added value: +"The column to order by, which must exist for the dataset kind: one of backlinks, dataforseo_rank, difficulty, domain, etv, id, intent, keyword, owned_rank_group, rank_group, referring_main_domains, search_volume, url, backlinks_spam_score, broken_backlinks, broken_pages, cpc, organic_keywords, rank_absolute, referring_domains, referring_pages. Omit to order by row id. Rows with no value sort last."
    • changedInput schema / properties / sort / type
      Previous value: -[
-  "string",
-  "null"
-]New value: +"string"
  2. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already establish the safe read-only/idempotent profile, but the description goes well beyond them: it discloses pagination (50 default, next_cursor with the same sort), staleness ('rows are as saved at research time and are never refreshed'), and a substantive correctness warning that referring-domain/page totals are provider aggregates and must not be reported as a link list. It also notes cross-project datasets are refused.

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?

Purpose and routing lead, and the caveats (aggregate totals, staleness) follow in a logical order. It is a dense paragraph with several clauses, but each sentence carries distinct, non-redundant 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, the description still conveys the return shape (dataset metadata plus a page of rows, each with a citeladder:// id for fetch), the pagination contract, and the key semantic caveats. Nothing an agent needs to call and interpret this tool is missing.

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 coverage is 100%, so parameters are already documented and a 3 would be the floor. The description adds value above the schema by specifying the default page size (50) and that subsequent pages share the same sort, details the schema's 'default page size' and cursor fields leave implicit.

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 and resource ('Read the rows of one saved keyword or backlink research dataset') and pins its place in the family by naming read_search_intelligence as the source of the id. An agent can distinguish it from read_search_intelligence and read_query_evidence without opening any 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?

Explicitly routes the agent: use read_search_intelligence to choose the dataset, this tool to read its rows, and read_query_evidence for the project's own Search Console queries. The prerequisite ('after read_search_intelligence has named it') is stated, not inferred.

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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