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check_serp

Check the live Google search results for any keyword: the top 10 organic results (rank, domain, title), the People Also Ask questions, related searches, and which non-organic blocks occupy the page, so you can tell whether a query has an AI Overview or local pack slot at all. Public data, no catalog entry required. A keyword with genuinely no results returns empty arrays, which is a real finding, not an error.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordYesSearch query, e.g. "roof repair denver"

TDQS

A4.5/5.0
Behavior5/5

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

No annotations exist, so the description carries the full burden. It goes beyond a simple action by disclosing the return payload structure (organic results with rank, domain, title; PAA; related searches; non-organic blocks) and explicitly handling the edge case where empty arrays are a valid finding, not an error. It also states 'Public data' which implies no auth requirements.

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?

Two sentences deliver a wealth of information without fluff. The first sentence front-loads the action and enumerates outputs; the second covers access and edge-case behavior. 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?

With one required parameter and no output schema, the description fully compensates by detailing what the tool returns, including specific components (organic, PAA, related, blocks) and the empty-array behavior. It's sufficient for an agent to set expectations and interpret results correctly.

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?

The schema already provides 100% coverage for the single keyword parameter, including an example. The description doesn't add extra syntax or semantics for the parameter itself, but given the high schema coverage, the baseline of 3 is appropriate. No further detail is necessary.

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 opens with a specific verb and resource: 'Check the live Google search results for any keyword'. It then enumerates the exact outputs (top 10 organic results, People Also Ask, related searches, non-organic blocks) and the purpose (detecting AI Overview or local pack slots). This clearly distinguishes it from siblings like check_aeo_record, which suggests a different record-based operation.

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?

The description provides implied usage context: 'any keyword' indicates broad applicability, and 'Public data, no catalog entry required' signals that this tool is a lightweight, on-demand check without prerequisites. It doesn't explicitly name alternative tools or exclusion criteria, but the context is clear enough for an agent to know when this tool fits.

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

A4/5.0
Disambiguation5/5

Each tool targets a distinct resource or action: checking AEO records, reading SERP data, generating records, retrieving facts, citations, competitor snapshots, engine visibility, keyword gaps, etc. Even similar-sounding tools like get_citations and get_cited_domains are clearly delineated by their descriptions. No two tools appear to overlap in purpose.

Naming Consistency5/5

All tool names follow a consistent verb_noun convention using snake_case: check_*, get_*, generate_*, propose_*, run_*, search_*. There is no mixing of casing or verb styles, and each name instantly communicates the action and subject.

Tool Count5/5

With 14 tools, the server is well-scoped for the domain of AI citation optimization. Each tool contributes a unique capability, and the count is neither sparse nor bloated. The number fits comfortably within the ideal 3-15 range for a focused server.

Completeness4/5

The tool surface covers the core lifecycle: checking existing records, generating new records, auditing and fixing sites, retrieving data, analyzing gaps, and proposing briefs. Minor gaps exist such as no direct tool for claiming a domain or managing tracked keywords/competitors, but those may be external to the MCP server. Overall, the set supports the intended workflows well.

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