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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. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare safety (readOnly, idempotent, non-destructive). The description adds significant behavioral details: response structure, decay computation method, snapshot gaps, and that history depth is bounded by 60-day TTL. No contradictions.

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 well-structured with sections, but slightly verbose. It effectively front-loads the key question. A minor reduction in length could improve conciseness without losing clarity.

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 lack of an output schema, the description thoroughly documents the response structure (tracked, expired, snapshot_dates) and explains computed fields. It covers all behavioral aspects needed for correct usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for both parameters. The description adds default values (14 for days, '1wk' for window), valid range for days (2-30), and clarifies 'window' as snapshot family. This adds meaningful context beyond the schema.

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's core purpose: analyzing edge persistence and decay over time. It uses specific verbs ('answers', 'tracks') and distinguishes from sibling 'polymarket_edges' by focusing on time-series analysis rather than current edges.

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 provides contextual guidance (e.g., a fresh wide edge vs 3-week-old wide edge are different) and explains limitations (snapshot TTL, gaps). However, it lacks explicit 'when not to use' or alternative tool recommendations.

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

A3.6/5.0
Disambiguation2/5

Multiple research entry points (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and company analysis tools (entity_profile, compare_entities, recent_changes, bet_research) overlap heavily; the five polymarket_* tools also require careful reading to distinguish. Descriptions are detailed, but an agent must parse long disambiguation text to avoid mis-selection.

Naming Consistency2/5

Names mix verb-first (list_subscriptions, validate_claim, remember), noun-first (entity_profile, polymarket_arbitrage), product-prefixed (ask_pipeworx, pipeworx_feedback), and brand-prefixed (diffbot_company, diffbot_extract). No consistent verb_noun pattern across the set; polymarket_* and pipeworx_* prefixes are internally consistent but the overall scheme is chaotic.

Tool Count2/5

33 tools is well beyond the 3-15 sweet spot and in the 'too many' range. Many tools are meta-variants (4 ask_pipeworx flavors, 5 polymarket tools, 2 AI-visibility tools) that could be consolidated.

Completeness3/5

The data-research, prediction-market, memory, and subscription subdomains are each fairly complete at a meta level, with few dead ends. Gaps include no subscription update (delete + recreate required), no explicit memory update, and no direct way to execute an arbitrary discovered Pipeworx tool aside from routing through ask_pipeworx.

Resources