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Thegamesdb

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

A4.7/5.0
Behavior5/5

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

The description explains the internal behavior: it probes each entity with 'ai_visibility_check' and aggregates results into a ranked list with score, confidence, and signal density. This adds significant context beyond the annotations, which already indicate the tool is read-only, idempotent, and non-destructive.

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?

The description is two sentences, each dense with information. The first sentence states purpose and method; the second provides a use case and return format. No unnecessary words, perfectly front-loaded.

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 4 parameters with 100% schema coverage and no output schema, the description fully explains the return format (ranked list with fields). It also references the internal use of a sibling tool and the annotations cover safety. The description is complete for an agent to understand behavior and output.

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. The description adds meaning: it clarifies the 'entities' array (first is subject, rest competitors) and explains that 'models' includes 'workers-ai' or 'anthropic', with _apiKey required only for 'anthropic'. The 'context' parameter is explained as disambiguation. This goes beyond the 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 specifies the verb 'compare' and the resource 'AI visibility across multiple entities'. It clearly distinguishes from sibling 'ai_visibility_check' (single entity) by emphasizing side-by-side comparison and ranking. Example use case further clarifies its purpose.

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 explicitly positions the tool for competitive AI-marketing audits and provides an example question. It implies when to use (when comparing multiple entities) but does not explicitly state when not to use or list alternatives. However, the sibling context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Thegamesdb toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

The set contains several tightly overlapping clusters: ask_pipeworx vs ask_pipeworx_beta (currently identical) vs ask_pipeworx_grounded, five polymarket_* tools with related purposes, and entity_profile vs compare_entities vs recent_changes covering similar company-research ground. The four game tools are distinct but swamped by the unrelated Pipeworx majority, making correct tool selection genuinely difficult.

Naming Consistency2/5

No coherent naming scheme spans the set: snake_case verb_noun (get_game, list_platforms, scan_dependency) coexists with verb_prefix descriptors (ask_pipeworx, generate_llms_txt), domain-prefixed nouns (polymarket_edges, pipeworx_trending), and bare verbs like recall and forget. Even within the Pipeworx cluster, styles vary unpredictably (ask_pipeworx vs pipeworx_feedback vs scan_competitor_ai_presence).

Tool Count2/5

35 tools is far too many for a server named Thegamesdb, where only 4 of 35 tools relate to the game database at all. The remaining 31 tools constitute a broad Pipeworx data platform with heavy internal overlap, making the surface feel bloated rather than well-scoped.

Completeness2/5

For the declared TheGamesDB domain, coverage is minimal: search, get-by-id, and list genres/platforms, with no per-platform game listings, images/artwork, or updates/refresh functionality. If the true domain is Pipeworx data access, the surface is fairly complete, but as presented under Thegamesdb there are major gaps and a severe identity mismatch.

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