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Glama

Malwarebazaar

by io.github.pipeworx-io

AI Visibility Check

ai_visibility_check
Read-onlyIdempotent

Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass _apiKey to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityYesThe thing to ask about. Brand/business name, product name, person, or topic. E.g. "Pipeworx", "OpenInvoice", "Acme Corp pricing".
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key (sk-ant-...) — only needed if "anthropic" is in models. Passed straight through to api.anthropic.com.
contextNoOptional: a phrase locating the entity (e.g. "Boston restaurant", "B2B SaaS"). Helps disambiguate common names.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.7/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint), the description adds key behavioral details: default model, BYO key for Anthropic, and the return structure (per-model {score, confidence, signals, raw_response} + combined view). No 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?

Two efficiently structured sentences: first sentence states purpose and scoring, second sentence covers model options and return format. Front-loaded with key info, no wasted words.

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?

Despite no output schema, the description explicitly states the return format and covers all parameters. For a 4-parameter tool with 100% schema coverage, this is fully adequate.

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 description coverage is 100%, so baseline is 3. The description adds value by explaining default model, _apiKey requirement only for Anthropic, and clarifies that 'context' helps disambiguate. This raises it to 4.

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 probes LLMs for knowledge about an entity and scores visibility (0-100). It names the default model and optional Anthropic probe, distinguishing it from sibling tools like scan_competitor_ai_presence.

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 specifies use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. While it doesn't explicitly exclude alternatives, the context is clear enough for an agent to decide when to use it.

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

B3.3/5.0
Disambiguation1/5

The server is named Malwarebazaar, yet 28 of 36 tools have nothing to do with malware—they cover general data lookup, SEC filings, Polymarket betting, memory, and npm scanning. Even within the malware tools, search_family, search_signature, search_tag, recent_samples, and get_sample_info overlap heavily, and the Pipeworx tools include near-duplicates like ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded. An agent cannot reliably pick between these without reading lengthy descriptions.

Naming Consistency2/5

A few tools follow verb_noun patterns (search_family, search_tag, get_sample_info, list_subscriptions), but the set mixes styles: ask_pipeworx vs deep_research vs entity_profile vs polymarket_arbitrage vs generate_llms_txt vs scan_dependency. Prefixes are inconsistent (ask_*, polymarket_*, pipeworx_*, search_*, scan_*, get_*, list_*, recent_*), and there is no predictable convention tying names to their domain.

Tool Count2/5

36 tools is far beyond what a MalwareBazaar MCP server should expose; most of the tools actually belong to a separate Pipeworx data platform, with only 5-6 malware-specific tools. The count is heavy and unfocused, especially for a server whose name implies a single malware-intel corpus.

Completeness2/5

For the malware domain, the set covers lookup by hash, family, signature, tag, and recent samples, but lacks obvious operations like submitting a sample, downloading a sample, or getting detailed YARA rule hits. For the broader Pipeworx domain, the surface is sprawling and overlaps heavily (ask_pipeworx vs deep_research vs validate_claim vs bet_research), so the completeness is uneven—deep in some niches, missing core malware workflow actions.

Resources