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Glama

Malwarebazaar

by io.github.pipeworx-io

Compare Entities

compare_entities
Read-onlyIdempotent

"Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type: "company" or "drug".
valuesYesFor company: 2–5 tickers/CIKs (e.g., ["AAPL","MSFT"]). For drug: 2–5 names (e.g., ["ozempic","mounjaro"]).

Schema Changelog

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

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds rich behavioral details: data sources, handling of off-calendar fiscal years, sorting by primary metric, and citation URIs. 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is relatively long but front-loaded with example invocations and key instructions. Every sentence adds meaningful information, though it could be slightly more concise without losing value.

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 the tool's complexity (two entity types, no output schema), the description comprehensively explains input requirements, data sources, sorting, and output format (paired data + citation URIs). It leaves no essential gaps.

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%, baseline 3. Description adds value by interpreting the 'type' parameter as selecting data source and specifying that 'values' accepts tickers/CIKs for companies and drug names, with min/max constraints clearly stated.

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 specifies the tool's function: side-by-side comparison of 2-5 companies or drugs, with distinct data sources (SEC EDGAR for companies, FAERS for drugs). It distinguishes itself from sibling tools like entity_profile and sequential single-pack lookups.

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 states to prefer this tool over sequential lookups for comparisons, provides example queries, and explains sorting behavior. It lacks explicit when-not-to-use guidance but is clear on context.

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

All Malwarebazaar toolsInspect
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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