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chess

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
Behavior5/5

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

Discloses it 'Probes each entity (your brand + N competitors) with ai_visibility_check' and 'ranks by score', plus the return format 'ranked list with score, confidence, signal density per entity' — adding behavioral depth beyond the readOnly/idempotent annotations.

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, front-loaded with the primary action, no fluff. Each sentence adds either function, internal process, or usage context.

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?

Given no output schema, the description covers both behavior and return format. It also explains the relationship to ai_visibility_check and gives a practical example, making it self-contained.

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 already covers all 4 parameters at 100% with detailed descriptions. The tool description adds no extra parameter semantics beyond a passing 'your brand + N competitors' that's already in the entities field description.

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?

Description opens with 'Compare AI visibility across multiple entities side-by-side' — a specific verb+resource+scope. It distinguishes from sibling ai_visibility_check by explicitly stating it probes each entity and ranks them.

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?

Provides a concrete use case: 'Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?"' and explains the multi-entity scope. It doesn't explicitly state when not to use, but the 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

B3.4/5.0
Disambiguation2/5

The server is named 'chess', yet none of the 34 tools relate to chess. An agent looking for chess functionality would find all tools irrelevant. While individual tool descriptions are clear, the server's name creates a fundamental disambiguation problem: the tool set does not match the server's apparent purpose.

Naming Consistency4/5

Tool names within the set follow a consistent snake_case pattern with descriptive verbs (e.g., ask_pipeworx, deep_research, resolve_entity). There are no mixed conventions. However, the server name 'chess' is completely inconsistent with the tool names, which all suggest data research rather than chess.

Tool Count1/5

For a server named 'chess', 34 tools is wildly excessive. Even for a data research server, the count is high, but the server's name implies a narrow chess domain, making the count inappropriate. The tools cover broad topics like SEC filings, Polymarket, and weather, none of which belong in a chess server.

Completeness1/5

The server claims to be about chess, but there are zero chess-related tools. The tool set is completely incomplete for its stated purpose. As a data research server, completeness might be high, but that is irrelevant given the server name.

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