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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

A4.4/5.0
Behavior5/5

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

Annotations declare readOnly, openWorld, idempotent, and non-destructive. The description adds valuable context: data from daily snapshots with gaps, 60-day TTL, decay computed from daily closes, and response structure. 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 clear sections for response fields and limits. It is somewhat long but every part adds value; could be slightly more concise.

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?

With no output schema, the description fully explains the return structure (tracked, expired, snapshot_dates fields) and limitations (60-day TTL, gaps, decay calculation). It is complete for a tool of this complexity.

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 coverage is 100% and both parameters are adequately described in the schema. The description adds minor clarification (e.g., 'max 30' vs schema's 'clamp 2-30') but does not significantly enhance meaning 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 purpose: providing edge persistence and decay telemetry from daily snapshots. It answers a specific question about how long edges have existed and whether they are shrinking, distinguishing it from sibling tools that list 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 explains the use case (differentiating fresh vs. old edges) but does not explicitly exclude alternative tools or provide when-not-to-use guidance. It implies usage context well.

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
Disambiguation2/5

Several tools occupy nearly the same role: ask_pipeworx and ask_pipeworx_beta are described as behaviorally identical, ask_pipeworx_grounded and deep_research both route questions across the same large catalog, and the Polymarket tools overlap heavily in intent. The ActiveCampaign list/get tools are distinct, but an agent facing 37 tools would frequently struggle to choose the right research or betting tool.

Naming Consistency2/5

Some clusters are internally consistent (list_*, ask_pipeworx_*, polymarket_*), but the server overall mixes bare verbs like remember and forget, noun phrases like entity_profile and deep_research, and brand-prefixed names like pipeworx_trending and polymarket_arbitrage. There is no unified naming convention across the tool set.

Tool Count2/5

37 tools is excessive for an ActiveCampaign integration, and only 6 of them actually relate to ActiveCampaign. The rest are a broad Pipeworx data, research, and prediction-market utility suite, so the count is not well-scoped to the server's stated purpose.

Completeness1/5

As an ActiveCampaign server, the surface is severely incomplete: it only provides read-only list/get operations and no create, update, delete, send, tag, or workflow-management tools. The unrelated Pipeworx tools may be feature-rich, but they do not fill the gaps in the named ActiveCampaign domain.