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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. 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?

The description adds significant detail beyond annotations: it explains data sources (daily polymarket_edges snapshots, 60-day TTL), response structure (tracked, expired, snapshot_dates), and limitations (history depth, decay from daily closes). Annotations already indicate readOnly and idempotent, so the description focuses on operational behavior.

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 front-loaded with the key question and then details the response. It is well-organized but somewhat verbose. Every sentence adds value, but could be tightened slightly. Overall, it effectively communicates without being overly brief.

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 return structure (tracked, expired, snapshot_dates with fields). It covers all necessary information for an agent to understand what the tool returns and how to interpret results, given the moderate complexity.

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%, but the description adds default values (14 days, '1wk'), clamp range (2-30), and explains the meaning of 'window' as snapshot family. This goes beyond the schema's brief description, providing practical usage context. Without these, the agent might misinterpret the parameters.

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 it provides 'edge persistence and decay telemetry' and answers 'how long has this edge existed and is it shrinking?'. It distinguishes from a tool like polymarket_edges by emphasizing historical behavior rather than current edges. The specific contrast between fresh and old wide edges clarifies the unique value proposition.

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 implies usage when needing historical edge decay analysis, but does not explicitly state when to avoid using it (e.g., when current edges only are needed). The context of sibling tools like polymarket_edges suggests alternative, but no direct guidance is given. The examples of trade differences provide implicit context.

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

A3.8/5.0
Disambiguation3/5

Most tools understandably fall into distinct clusters (BLS data, Polymarket, entity research, memory, subscriptions) and have detailed descriptions, but there is real overlap among the query entry points: ask_pipeworx and ask_pipeworx_beta are currently identical, and ask_pipeworx, deep_research, and validate_claim can all answer similar factual questions. The descriptions help an agent choose, but the set still contains more than a couple of near-duplicate paths.

Naming Consistency3/5

All names are lowercase snake_case and several clusters share domain prefixes like bls_, polymarket_, and pipeworx_, which keeps the surface readable. However, the semantic naming pattern is mixed: verb+noun names like resolve_entity and list_subscriptions coexist with noun phrases like entity_profile, recent_alerts, and bls_latest, plus brand-led names like ask_pipeworx and polymarket_edges.

Tool Count2/5

At 36 tools, the set is well past the 25+ threshold for a heavy tool surface, and several tools inflate the count: duplicate ask_pipeworx variants, multiple overlapping Polymarket scanners, and one-off meta helpers. The broad Pipeworx scope explains some of the breadth, but the redundancy makes the set feel bloated.

Completeness4/5

The set covers its core workflows well: data lookup, grounded verification, entity profiling and comparison, BLS series access, Polymarket research, subscriptions, memory, and feedback. Minor gaps remain, such as no subscription-editing tool, no dedicated citation-reader tool, and no general web-search tool, but ask_pipeworx acts as a catch-all router that lets agents work around most of them.

Resources