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Glama

Malwarebazaar

by io.github.pipeworx-io

Polymarket Edge Tracker

polymarket_edge_tracker
Read-onlyIdempotent

Edge persistence and decay telemetry built from daily polymarket_edges snapshots. Answers "how long has this edge existed and is it shrinking?" — a fresh wide edge and a 3-week-old wide edge are different trades (the latter is wide for a reason nobody is willing to take). Args: days (lookback, default 14, max 30), window (snapshot family, default "1wk"). RESPONSE: tracked[] = every opportunity in the LATEST snapshot with its full edge_pp_net time-series across prior snapshots, first_seen, trend (new | widening | stable | decaying) and decay_pp_per_day (both computed on |edge_pp_net| — the value itself is signed by trade direction, negative = SELL YES); expired[] = opportunities that appeared in earlier snapshots but are GONE from the latest (closed, resolved, or arbed away) with their lifespan_days — the median lifespan is your competition clock; snapshot_dates[] = which days actually have data (snapshots are written when polymarket_edges runs on a cache-miss, so gaps mean nobody scanned that day). LIMITS: history depth is bounded by the 60-day snapshot TTL and starts from when snapshotting was enabled; decay numbers come from daily closes of edge_pp_net (net of default slippage), not intraday.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoLookback in days (default 14, clamp 2-30).
windowNoWhich polymarket_edges window family to read snapshots for: 24hr | 1wk | 1mo (default 1wk).

Schema Changelog

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

  1. Added
  2. Removed
  3. Added

TDQS

A4.3/5.0
Behavior5/5

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

Annotations already declare the tool is read-only and non-destructive. The description adds rich behavioral details: response structure (tracked[], expired[], snapshot_dates[]), sign meaning, decay computation, cache-miss gaps, and median lifespan. No contradictions with annotations.

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?

Description is well-organized with clear sections (RESPONSE, LIMITS) and front-loaded with the key question. It is slightly verbose but every sentence adds value. No wasted words.

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?

Without an output schema, the description thoroughly explains the return format and fields. It covers limits and snapshot gaps. Lacks error handling or permission requirements, but for a read-only tool with annotations, it is sufficient.

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%, so description adds minimal extra meaning. It clarifies 'days' max as 30 (schema says clamp 2-30) and 'window' as 'snapshot family', but schema already documents values. Benefit is marginal.

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 provides 'edge persistence and decay telemetry' and answers a specific trading question. It distinguishes from sibling tools like 'polymarket_edges' by focusing on the time-series behavior of edges, not just current snapshots.

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 context on why edge persistence matters ('a fresh wide edge and a 3-week-old wide edge are different trades') and mentions limits (60-day TTL), but does not explicitly state when NOT to use this tool or name alternative tools for current edge data.

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