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The Guardian

by io.github.pipeworx-io

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

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. The description adds concrete behavior: 'Probes each entity with ai_visibility_check, ranks by score, surfaces which is most/least recognized' and specifies return fields (score, confidence, signal density). No contradictions.

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?

Three sentences with no fluff. First sentence states purpose, second explains process, third gives a concrete example. Every sentence 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?

No output schema exists, but the description adequately explains the return format (ranked list with score, confidence, signal density). Parameters are fully covered by schema and additional semantic hints. The description is complete for a tool of this complexity.

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 baseline is 3. Description adds value by explaining the first entity is the 'subject' and rest are competitors, and that context disambiguates common names. This goes beyond the schema descriptions.

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' using a specific verb and resource. It distinguishes from siblings like ai_visibility_check (single entity) and compare_entities (generic) by emphasizing the side-by-side comparison for competitive audits.

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 a clear use case: 'Useful for competitive AI-marketing audits: does Claude know about us as well as our competitors?' It does not explicitly list when not to use or alternative tools, but the context strongly implies appropriate scenarios.

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

B3.4/5.0
Disambiguation2/5

Many tools have overlapping purposes and similar names, especially the Pipeworx query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and the Polymarket tools (polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, etc.). The Guardian-specific tools are distinct but are outnumbered by these confusing clusters, making it hard for an agent to reliably select the right tool.

Naming Consistency1/5

Tool names follow no consistent pattern: some are snake_case (ai_visibility_check, ask_pipeworx), some are verbs without objects (item, tags, forget, recall), and some are inconsistent in style (compare_entities vs. bet_research). There is no unifying naming convention across the set.

Tool Count2/5

With 36 tools, the server is bloated for its implied purpose ('The Guardian' suggests a focused news outlet). Many tools belong to unrelated domains (Pipeworx data platform, Polymarket prediction markets), making the count feel excessive and unfocused. A news-specific server should have far fewer tools.

Completeness1/5

The tool set lacks a coherent domain. The Guardian news tools are complete (search, item, sections, etc.), but the massive inclusion of Pipeworx and Polymarket tools creates dead ends and gaps (e.g., no direct tool to list all prediction markets or search patents). The surface feels like a random collection rather than a designed whole.

Resources