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Ipquery

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?

Annotations declare it's read-only, idempotent, and non-destructive. The description adds rich behavioral details: response structure (tracked, expired, snapshot_dates), field meanings (trend, decay_pp_per_day), limits (60-day TTL, no intraday data), and computational specifics (daily closes). 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 dense but well-organized, front-loading the core question and then detailing output format and limitations. Every sentence adds value, though the length is justified by the amount of information conveyed.

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 documents the return values (tracked, expired, snapshot_dates with fields and interpretation). It also explains limitations and data source. Parameter coverage is complete via schema. The tool is well-specified for an AI to use 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?

Schema contains full descriptions, but the tool description adds valuable context: 'days' is lookback with default 14 and max 30 (plus clamp), 'window' is snapshot family with default '1wk'. This extra detail helps an agent set appropriate parameters 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 states it tracks edge persistence and decay from daily snapshots, answering 'how long has this edge existed and is it shrinking?' It distinguishes from sibling tools like polymarket_edges by focusing on historical persistence 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?

Provides explicit context on when to use (evaluating edge longevity) and parameter defaults with limits. It does not explicitly state when not to use, but the purpose is clear enough to guide appropriate invocation.

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

Many tools are clearly distinct, but the existence of multiple ask_pipeworx variants (standard, beta, grounded) and the overlapping check_risk/lookup_ip tools create meaningful selection ambiguity. Description differentiation helps but doesn't fully resolve it.

Naming Consistency3/5

All names use snake_case, but conventions vary between verb-noun (lookup_ip, compare_entities), noun phrases (entity_profile, recent_alerts), and domain-prefixed names (polymarket_edges, pipeworx_trending). It's readable but not a consistent pattern.

Tool Count2/5

At 33 tools the surface is large, with duplicated ask_pipeworx variants, a modest IP lookup core, and a large number of meta/management tools (memory, subscriptions, onboarding, feedback). Even with a broad scope this feels overloaded, and it is well above the 25+ threshold.

Completeness4/5

The query domain is well covered: entity profiles, comparison, claim validation, deep research, changes, plus memory and subscription management. Minor gaps exist (no direct subscription update, no raw citation fetch utility), but agents can work around them.

Resources