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Scan Competitor AI Presence

scan_competitor_ai_presence
Read-onlyIdempotent

Compare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?". Returns ranked list with score, confidence, signal density per entity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key — only if "anthropic" is in models. Passed to api.anthropic.com per probe.
contextNoOptional shared context applied to every probe (e.g. "B2B SaaS", "Boston restaurant"). Disambiguates common names.
entitiesYesArray of 2-8 entities to compare (brand/business/product names). First entry treated as the "subject" for narrative; rest are competitors.

TDQS

A4.2/5.0
Behavior4/5

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

Beyond readOnlyHint etc., it describes the internal process (probing with ai_visibility_check, ranking) and output (score, confidence, signal density per entity). Adds meaningful behavioral context.

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 covering purpose, method, use case, and output. No wasted words; front-loaded with action.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a multi-entity comparison tool, the description covers input constraints, process, and output format. No output schema but return fields are listed. Sufficient for agent selection.

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 baseline 3. Description adds value by linking entities to 'your brand + N competitors' and giving context examples, but doesn't significantly expand beyond 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?

The description clearly states 'Compare AI visibility across multiple entities side-by-side' with a specific verb and resource. It distinguishes from sibling tools like ai_visibility_check (single entity) by emphasizing multi-entity comparison and ranking.

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?

It provides a concrete use case ('competitive AI-marketing audits') and implies the alternative ai_visibility_check for single probes. Lacks explicit 'when not to use' but context is clear.

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

A3.9/5.0
Disambiguation2/5

Several tools occupy nearly identical roles (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools), and ask_pipeworx_beta is explicitly the same router as ask_pipeworx. Company-data tools (entity_profile, compare_entities, recent_changes, validate_claim) and the Polymarket family also overlap heavily, making selection error-prone despite detailed descriptions.

Naming Consistency5/5

Tool names are consistently lowercase snake_case with a verb_noun pattern (search_notices, get_notice, find_a_tender_recent, validate_claim). Even longer names like polymarket_edge_tracker and ask_pipeworx_grounded follow a predictable style with no camelCase or mixed conventions.

Tool Count2/5

36 tools is already excessive for a focused server, and the 'Uk Contracts' name covers only five of them (search_notices, recent_notices, get_notice, find_a_tender_recent, find_a_tender_notice). The remaining 31 are unrelated Pipeworx/Polymarket/AI-marketing utilities, so the count badly mismatches the apparent scope.

Completeness4/5

For UK public procurement, the five relevant tools provide search, recent listing, and full-detail retrieval for both Contracts Finder and Find a Tender Service, covering the core workflows well. Minor gaps include no tender-specific alert/subscription support and no server-side keyword search for the high-value FTS feed.