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Malwarebazaar

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

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so safety is transparent. Description adds that it probes each entity, ranks by score, and returns a ranked list with score, confidence, signal density, fully explaining the behavior without contradiction.

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?

Description is three sentences, front-loaded with the core action, immediately followed by purpose and example. No redundant or extraneous information; every sentence adds value.

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?

Input schema is fully documented, annotations cover safety, and description details the return format (ranked list with score, confidence, signal density). No output schema exists, but description sufficiently explains what to expect, making the tool complete for its complexity level.

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 is 3. The description provides high-level context for parameters (e.g., 'first entry treated as subject', 'context disambiguates'), but these details are already present in the input schema descriptions, adding no new meaningful information beyond what the schema provides.

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 clearly states the tool compares AI visibility across multiple entities side-by-side, using ai_visibility_check internally and ranking results. It distinguishes itself by targeting competitive audits, which is specific and different from sibling tools like ai_visibility_check (single probe).

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?

Explicitly states the tool is for competitive AI-marketing audits and gives an example query. It implies single-entity checks should use ai_visibility_check, though doesn't explicitly list alternatives. Guidance is clear for when to use.

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

A3.5/5.0
Disambiguation2/5

Several tools are near-duplicates: ask_pipeworx_beta is described as currently identical to ask_pipeworx, and ask_pipeworx_grounded is the same router with extra verification. Malware sample searches also overlap across search_family, search_tag, search_signature, and recent_samples, while ai_visibility_check is wrapped by scan_competitor_ai_presence.

Naming Consistency3/5

The set mostly uses snake_case and verb-first names like get_sample_info and validate_claim, which is helpful. However, conventions are mixed across ask_pipeworx*, polymarket_*, pipeworx_*, search_*, and noun-style names like recent_samples and entity_profile, so there is no single predictable pattern.

Tool Count2/5

36 tools is already over the comfortable range, but the bigger issue is that the server is named Malwarebazaar while only about five tools actually deal with malware. The remaining tools belong to an unrelated data-research, prediction-market, memory, and subscription platform, making the count inappropriate for the server's apparent purpose.

Completeness2/5

For the MalwareBazaar domain, the set covers metadata lookups and filtered sample searches but lacks sample submission, retrieval, or deeper analysis workflow. The rest of the tool surface targets unrelated domains, so there is no coherent, complete lifecycle for either malware intelligence or the broader feature set.

Resources