Skip to main content
Glama

Statcan

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

A4.6/5.0
Behavior5/5

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

The description adds significant behavioral context beyond annotations: it explains the data source (daily snapshots), response structure (tracked, expired, snapshot_dates), limitations (60-day TTL, snapshot frequency based on cache misses), and how decay is computed. 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?

The description is appropriately detailed and well-structured, using a question to frame purpose and listing response fields in a clear format. It is not overly verbose given the complexity, though minor trimming could be possible.

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 that the tool has no output schema, the description fully explains the response structure (tracked, expired, snapshot_dates) and relevant limits. It provides complete context for an AI agent to use the tool correctly.

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?

The schema already provides descriptions for both parameters (100% coverage). The description adds value by clarifying defaults, bounds (days clamped 2-30), and explicit values for window ('24hr', '1wk', '1mo'), which enriches understanding beyond 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: to provide edge persistence and decay telemetry from daily snapshots. It distinguishes itself by focusing on historical tracking and the lifespan of edges, contrasting with sibling tools like polymarket_edges which likely provide 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 explains when to use the tool (to understand edge longevity and decay) and implies its value by contrasting fresh vs. old edges. However, it does not explicitly state when not to use it or provide direct comparisons to sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Statcan toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation2/5

Several tools are near-duplicates: ask_pipeworx_beta is explicitly identical to ask_pipeworx right now, ask_pipeworx_grounded is the same router with an extra extraction pass, and deep_research/ask_pipeworx overlap for broad questions. The prediction-market tools and the StatCan series/cube/indicator tools also have fuzzy boundaries despite their detailed descriptions.

Naming Consistency3/5

Names consistently use snake_case, but the set mixes verb-first names (resolve_entity, validate_claim, subscribe) with domain-prefixed noun-first names (statcan_*, polymarket_*, pipeworx_*) and one-off names like ai_visibility_check and generate_llms_txt. The domain prefixes help navigation, but there is no single predictable pattern and the ask_pipeworx_* suffix variants break the prefix convention.

Tool Count2/5

38 tools is well beyond the 25+ threshold, and the server named Statcan carries only 8 StatCan-specific tools alongside general Pipeworx routing, prediction-market analysis, AI visibility, dependency scanning, memory, and subscription features. This feels like several servers merged into one rather than a well-scoped StatCan interface.

Completeness4/5

Within the apparent StatCan data-access scope, the surface is solid: listing cubes, metadata, cube data, vector series, headline indicators, CSV URLs, and change detection cover the core workflows. The broader Pipeworx/analysis layers also include discovery, grounded lookups, entity resolution, validation, subscriptions, and memory, with only minor gaps like server-side StatCan search and no way to execute on prediction-market signals.

Resources