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get-seo-segment-counts

Read-only

Count the project's tracked keywords in each Keywords segment from the latest completed rank check. Rank segments: TOP_10 (rank 1-10), STRIKING (11-20), NOT_RANKING, and DROPPED (down 5 or more places, or out of the results). Search Console segments: LOSING_CLICKS (clicks down 30% or more), LOW_CTR (under half the expected click rate), and CANNIBALISED (two or more pages competing). A keyword can be in several. Read-only. Returns { total, segments }; segments is empty before the first completed check. To list a segment's keywords, pass segment to get-seo-keywords.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "advertiserProjectId": "<advertiserProjectId>",
      "context": "<context>",
      "llm_model": "<llm_model>"
    },
    "name": "get-seo-segment-counts"
  }
}

Input Schema

NameRequiredDescriptionDefault
engineNoOnly count keywords tracked on this search engine. Omit for all engines.
contextYesExplain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
llm_modelYesThe exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
advertiserProjectIdYesProject id from get-projects or get-project.

Schema Changelog

Changes observed during successful MCP inspections.

No schema history has been recorded yet.

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already state readOnlyHint=true and destructiveHint=false, and the description restates read-only harmlessly. Beyond that it adds real behavioral detail: segment thresholds, the overlap rule ("A keyword can be in several"), the return shape { total, segments }, and the pre-first-check empty state, none of which annotations supply. It stops short of only minor things like data freshness bounds, which is not applicable here.

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 is front-loaded, followed by segment definitions, read-only status, return shape, and sibling routing in a tight sequence. The enumeration of seven segments is long but each entry carries real disambiguation value, so almost every clause earns its place.

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?

There is no output schema, so the description's explicit return contract { total, segments } plus the empty-before-first-check caveat fills that gap. For a read-only counting tool with fully documented parameters, nothing an agent needs to call it 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 description coverage is 100%, so engine, advertiserProjectId, context, and llm_model are all self-documenting, which sets the baseline at 3. The description adds no parameter-level guidance beyond the segment codes; notably it does not explain the engine filter's interaction with counting, so it does not exceed the schema.

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?

"Count the project's tracked keywords in each Keywords segment from the latest completed rank check" gives a specific verb (count) plus a precisely scoped resource and data source. The exhaustive segment list (TOP_10, STRIKING, NOT_RANKING, DROPPED, LOSING_CLICKS, LOW_CTR, CANNIBALISED) makes it unmistakable from get-seo-keywords, which lists rather than counts.

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?

The final sentence explicitly routes the agent: "To list a segment's keywords, pass segment to get-seo-keywords," naming the alternative and the condition that selects it. The scope phrase "from the latest completed rank check" also tells the agent when this tool is meaningful versus other SEO reads.

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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