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econdata

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

A4.4/5.0
Behavior5/5

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

Despite annotations declaring readOnly/idempotent, the description goes far beyond them by detailing the 60-day snapshot TTL, cache-miss snapshot creation, gaps indicating no scan, and decay computed from daily closes net of slippage. This enriches the agent's understanding of edge cases and data provenance.

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 long but densely packed with useful information, structured logically from purpose to args to response to limits. There is no filler, though the parenthetical metaphor ('the latter is wide for a reason...') could be trimmed without losing essential meaning.

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 thoroughly explains the response structure: tracked[] with trend and decay fields, expired[] with lifespan, and snapshot_dates[] with gap semantics. It also covers limitations (TTL, daily closes), making the tool's behavior fully understandable without extra resources.

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?

The input schema already provides full descriptions for both parameters (days and window) with defaults and clamps. The description adds minimal new detail—'max 30' vs schema's 'clamp 2-30'—and otherwise repeats the schema information, so it stays at the baseline.

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 function: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots.' It uses a specific verb ('track') and resource (edge persistence/decay) and distinguishes itself from siblings by focusing on historical behavior 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?

It provides a clear usage context via the example question 'how long has this edge existed and is it shrinking?' and explains why this matters ('a fresh wide edge and a 3-week-old wide edge are different trades'). However, it does not explicitly mention alternative tools or when not to use this one, so it falls short of a 5.

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
Disambiguation4/5

Most tools have distinct purposes due to detailed descriptions. Some overlap exists, especially among research tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and prediction market tools, but boundaries are clear enough for an agent to differentiate.

Naming Consistency4/5

The majority follow a consistent verb_noun pattern with underscores (e.g., get_cpi, list_subscriptions). A few deviations exist (e.g., forget, recall, pipeworx_feedback using a prefix), but overall the pattern is predictable.

Tool Count2/5

With 34 tools, the server exceeds the typical well-scoped range of 3-15 tools. While it covers many domains, the high number makes the set feel heavy and harder to navigate, warranting a score of 2 according to calibration.

Completeness4/5

The tool set is comprehensive for its broad scope, covering economic data, company research, prediction markets, and utilities. Minor gaps exist (e.g., missing explicit GDP or stock quote tools), but the powerful ask_pipeworx meta-tool fills many gaps.

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