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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

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds behavioral detail: it reads from daily snapshots, provides trend/decay metrics, and notes data gaps and TTL bounds. 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 well-structured: purpose first, then args, then detailed response fields, then limits. Each sentence is informative, though slightly verbose. It front-loads the core question, making it efficient for agent scanning.

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?

Despite no output schema, the description fully explains the response structure (tracked[], expired[], snapshot_dates[]), data types, and limits. It covers all necessary context for a low-complexity tool with two optional parameters, leaving no ambiguity.

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 coverage is 100% with parameter descriptions. The description adds default values and extra context (e.g., 'snapshot family' for window, clamp range for days), enriching the schema without redundancy. Baseline is 3 due to high coverage, but the added defaults and response explanation justify a 4.

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: 'Answers how long has this edge existed and is it shrinking?' It specifies verb (track), resource (edge persistence from polymarket_edges snapshots), and distinguishes from sibling polymarket_edges by focusing on time-series and decay. The example of a fresh vs. 3-week-old edge reinforces its unique value.

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 context on when to use ('fresh wide edge vs. stale edge') and includes interpretation guidance. However, it does not explicitly state when not to use this tool or directly compare it to alternatives like polymarket_edges, though the distinction is implied. Limits are clearly stated.

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

A4/5.0
Disambiguation4/5

Most tools have distinct purposes, but ask_pipeworx and ask_pipeworx_grounded overlap in routing (differ only in answer mode), and the multiple Polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_kalshi_spread) could cause confusion without careful reading of descriptions.

Naming Consistency3/5

Naming mix of verb-first (ask_pipeworx, compare_entities) and noun-first (entity_profile, recent_changes) patterns. Most use snake_case consistently, but the pattern is not uniform—some tools are commands, others are descriptors. Notable deviations like 'pipeworx_feedback' and 'pmc' are missing here but the sample shows inconsistency.

Tool Count3/5

30 tools is on the high side, but the server covers a broad domain (data lookup, betting, AI, genomics, memory). Some tools could be merged (e.g., ask_pipeworx and its grounded variant), and the betting subdomain feels over-instrumented. The count is borderline between appropriate and heavy.

Completeness3/5

The server provides a wide range of operations for its diverse domains, but gaps exist: the genomics tools only offer basic metadata search (no download/analysis), and the betting tools lack historical data or backtesting. It covers common patterns but with notable omissions for a cohesive experience.

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