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Apology

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?

The description adds significant context beyond the annotations (readOnlyHint, etc.). It explains that data comes from daily snapshots with a TTL, decay uses daily closes not intraday, how expired opportunities are handled, and the sign convention for decay. This is thorough and honest about data sourcing and computation.

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 and front-loaded with the core purpose. While it is somewhat lengthy, all sentences add value. It is well-structured with clear sections for response fields and limitations, making it easy to parse.

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?

The description is exceptionally complete given the lack of output schema. It details the three response arrays (tracked, expired, snapshot_dates) and their fields, plus limitations and interpretation notes. An agent can fully understand what to expect without additional context.

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%, so baseline is 3. The description reiterates default and max values for days and default for window, which mostly mirrors the schema. No new semantic information is added beyond what is already in the input schema descriptions.

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 identifies the tool's purpose: providing edge persistence and decay telemetry from daily snapshots. It explicitly answers 'how long has this edge existed and is it shrinking?' and distinguishes itself from siblings by focusing on time-series analysis and trends, not just 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 provides clear context for when to use this tool (e.g., for historical edge analysis) and includes limitations like 60-day snapshot TTL and decay computation from daily closes. However, it does not explicitly state when NOT to use it or suggest alternative tools for other use cases.

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

A3.7/5.0
Disambiguation2/5

ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicates with beta explicitly identical to the stable version, causing potential misselection. ai_visibility_check and scan_competitor_ai_presence overlap heavily, and resolve_entity/discover_tools/ask_pipeworx all serve lookup purposes. Many tools are distinct, but the boundaries around the core query tools are blurry.

Naming Consistency2/5

Naming is inconsistent: mostly snake_case but mixed verb styles (ask_pipeworx vs pipeworx_feedback vs resolve_entity), brand prefixes applied irregularly, and no uniform convention (e.g., subscribe/unsubscribe/list_subscriptions vs forget/remember/recall vs polymarket_arbitrage/edges/edge_tracker). Some names are descriptive, but the set lacks a predictable pattern.

Tool Count2/5

32 tools is above the 25 threshold for a heavy surface, and the server mixes unrelated domains (data lookup, prediction markets, memory, subscriptions, AI visibility, apology generation). While a large data platform could justify many tools, the random inclusions (apology_generate, generate_llms_txt, scan_dependency) suggest a lack of scoping. Several tools could be consolidated without loss.

Completeness4/5

For the dominant data-research/prediction-market domain, coverage is strong: lookup, grounded verification, research, entity resolution, comparison, arbitrage scanning, fill risk, subscriptions, memory, and discovery are all present. Minor gaps exist (no direct account management beyond subscriptions, no tool to modify stored memories), but agents can mostly achieve their goals. The stray non-domain tools do not hurt completeness of the core platform.

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