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Bungie

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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and non-destructive. The description adds context: it explains the process of probing each entity with ai_visibility_check, ranking by score, and returning a ranked list. This goes beyond what annotations provide without contradicting them.

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 plus a parenthetical example, all essential. It is front-loaded with the main action and contains no filler. Every sentence adds value, making it concise and well-structured.

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?

The description explains the return format (ranked list with score, confidence, signal density) and the internal process. While parameters are fully documented in schema and annotations cover safety, the description could mention error conditions or entity count limits (though schema covers 2-8). It is largely complete for this complexity level.

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 value by clarifying that the first entity in 'entities' is treated as the 'subject' for narrative, with the rest as competitors. It also provides usage context for 'models' and '_apiKey' parameters beyond 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 the tool's purpose: compare AI visibility across multiple entities. It uses specific verbs like 'compare', 'probes', 'ranks', and 'surfaces'. It identifies the resource (AI visibility across entities) and distinguishes from sibling tools like ai_visibility_check which checks a single entity.

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 gives a clear use case: 'Useful for competitive AI-marketing audits' and provides a concrete example question. It implicitly distinguishes from single-entity checks by emphasizing multiple entities. However, it does not explicitly state when not to use or list alternative tools.

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

C2.7/5.0
Disambiguation3/5

The tool set mixes Destiny 2 game tools with a large suite of general data querying tools from Pipeworx. Within the data querying subset, tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research have overlapping purposes, making it hard for an agent to distinguish which to use. The Destiny tools are more distinct, but overall, the set has noticeable ambiguity.

Naming Consistency2/5

Tool names are highly inconsistent, mixing single-word names (character, clan), snake_case (ask_pipeworx, deep_research), and compound names with underscores (polymarket_arbitrage, scan_dependency). There is no uniform verb_noun or other predictable pattern, making it difficult for an agent to infer functionality from the name alone.

Tool Count2/5

With 43 tools, the server is bloated for its stated name 'Bungie'. Many tools are unrelated to Bungie (e.g., Pipeworx data tools, Polymarket tools), suggesting the server aggregates multiple domains without clear scoping. The tool count is too high for a coherent set focused on a single service.

Completeness2/5

For the Bungie game domain, the set is fairly complete (characters, clans, stats). However, the inclusion of numerous unrelated tools (financial, economic, prediction market) fragments completeness. The server lacks a clear domain, leaving gaps in both the Bungie-specific and the general data querying aspects when considered as a unified set.

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