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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

A4.6/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, etc.), the description adds valuable behavioral details: history depth bounded by 60-day snapshot TTL, cache-miss gaps, decay computed from daily closes, not intraday. This exceeds the annotation baseline.

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 dense with information but front-loaded with the core question. It could be more structured, but every sentence adds value without redundancy.

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 thoroughly explains the response format: tracked[], expired[], snapshot_dates[], and key fields like first_seen, trend, decay_pp_per_day, and lifespan_days. It covers limitations and nuances comprehensively.

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%, but the description adds context for 'days' (default 14, max 30) and 'window' (snapshot family, default '1wk'). This provides additional clarity beyond the schema alone.

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 defines the tool's purpose: edge persistence and decay telemetry from daily snapshots. It answers the specific question 'how long has this edge existed and is it shrinking?' and distinguishes itself from siblings like polymarket_edges and polymarket_arbitrage with a unique verb+resource.

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 context on when to use the tool, e.g., differentiating between fresh and old edges. However, it lacks explicit directives on when not to use it or alternatives among siblings, though the context is strong.

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

A3.9/5.0
Disambiguation3/5

Several tools cluster around similar purposes—the three ask_pipeworx variants, the five polymarket_* analysis tools, and the meta/discovery tools (discover_tools, suggest_questions, pipeworx_trending)—so an agent could plausibly call the wrong one. However, the descriptions are exceptionally detailed with explicit 'use this when' guidance, which mitigates most confusion.

Naming Consistency3/5

Names are almost all snake_case, but there is no consistent verb_noun or resource_action pattern: ask_pipeworx, entity_profile, remember, get_scoreboard, polymarket_fill_risk, etc. Pairs like remember/recall/forget and subscribe/unsubscribe are consistent, but the broader set mixes verbs, nouns, and prefixes (ask_, pipeworx_, polymarket_, get_, scan_) without a unified scheme.

Tool Count2/5

36 tools is well over the 25-tool 'heavy' threshold, and the set spans multiple unrelated domains: sports data, a general structured-data router, prediction-market analysis, memory storage, and user feedback. Many tools are meta or auxiliary (suggest_questions, pipeworx_feedback, remember/recall/forget) that don't clearly belong to the server's core purpose, making the set feel bloated.

Completeness3/5

For a sports-data server, core score/news/standings/team/schedule operations exist, but player stats, game details, injuries, and playoff brackets are missing. For the broader Pipeworx data platform the surface is extensive, but the mix of domains makes it hard to declare the set complete for any single stated purpose.