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get_competitive_snapshot

Get the latest competitive intelligence for a cataloged domain: the owner's most recent ranking-footprint snapshot, the AI-prompt vs Google demand table for its watched keywords, and each tracked competitor with their own latest snapshot. Read-only. Only domains whose owner has claimed them are exposed; anything else returns found:false. Counts are ranking FOOTPRINT, never traffic, and every volume is a modelled estimate. A null means no estimate has been collected, never zero.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYesCompany domain, e.g. acme.com

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden. It explicitly discloses read-only behavior, the found:false edge case for unclaimed domains, that counts are ranking footprint (not traffic), that volumes are modelled estimates, and that null means no estimate (not zero). This is exemplary behavioral disclosure.

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?

Each sentence earns its place: the first defines the main purpose, and the rest add distinct clarifying details (read-only, eligibility, data semantics, null meaning). It is well-structured and front-loaded, with no redundant content.

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?

Despite having only one parameter, no output schema, and no annotations, the description fully covers what the tool returns, under what conditions it returns found:false, and how to interpret the data. For this complexity level, it is complete and self-sufficient.

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?

The schema describes 'domain' simply as a company domain with an example. The description adds the essential semantics that the domain must be cataloged and claimed, which is critical for interpreting returned data. This goes beyond the schema's basic definition.

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 'Get the latest competitive intelligence for a cataloged domain' and then enumerates the exact components returned: the owner's ranking-footprint snapshot, the AI-prompt vs Google demand table, and tracked competitors' snapshots. This is a specific verb+resource that clearly distinguishes it from sibling tools like get_company_facts or get_engine_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?

The description provides clear eligibility conditions ('Only domains whose owner has claimed them are exposed; anything else returns found:false') and notes the read-only nature, which helps an agent decide when to call it. However, it does not explicitly mention alternatives or compare to sibling tools, so it stops short of full guidance.

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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