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Malwarebazaar

by io.github.pipeworx-io

Generate llms.txt

generate_llms_txt
Read-onlyIdempotent

Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesFull URL of the site to summarize, e.g. "https://example.com" or a specific landing page.
max_linksNoMaximum number of link entries to include (default 25, max 50).

Schema Changelog

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

  1. Added

TDQS

A4.4/5.0
Behavior5/5

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

Discloses that it fetches the page, extracts title/description/key links, and emits standard llms.txt markdown. Annotations already provide safety traits (readOnly, idempotent), so description adds valuable behavioral context 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?

Two sentences plus a bulleted use-case list. Every sentence adds value, no redundancy. Front-loaded with core purpose.

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?

Covers purpose, output format, and use cases adequately for a simple tool with full annotations. Lacks mention of error handling or invalid URLs, but not critical for selection.

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 description coverage is 100% with detailed parameter descriptions. Description adds only minor context (e.g., 'default 25, max 50' for max_links) beyond schema, so baseline 3 is appropriate.

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 verb 'Generate', resource 'llms.txt file', and context 'for any URL so AI crawlers can index the site cleanly'. It differentiates from siblings by specifying a unique output format and use case.

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 lists three use cases: getting a client's site indexed, drafting for own project, auditing competitor AI visibility. Lacks explicit when-not-to-use or alternatives, but use cases are clear and distinct from siblings.

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