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Arbeitnow

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).

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds substantial behavioral details: response structure (tracked, expired, snapshot_dates), field meanings (trend, decay_pp_per_day, lifespan_days), and limitations (60-day TTL, decay from daily closes). No contradictions.

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 detailed and front-loaded with purpose, but it is somewhat long. However, each sentence serves a purpose—explaining response format, fields, and limitations. Minor redundancy could be trimmed, but overall well-structured.

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?

No output schema, so the description must fully explain return values. It does so exhaustively: tracked[], expired[], snapshot_dates[] with field meanings and limitations. Highly complete for a read-only telemetry tool.

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 covers 100% of parameters with descriptions. The description adds value by specifying defaults (14 days, 1wk), constraints (max 30), and clarifying 'window' as 'snapshot family.' This goes 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 the tool's purpose: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots.' It answers a specific decision-making question and distinguishes itself from siblings like polymarket_edges by focusing on temporal aspects.

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 implicitly gives usage context by contrasting fresh wide edges with old ones, suggesting when to use this tool over raw edges. It does not explicitly list alternatives or state when not to use, but the context is clear enough.

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

A4.2/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose, from job searching to company research to bet analysis. Even closely related tools like ask_pipeworx and ask_pipeworx_grounded are differentiated by one being hallucination-resistant. Memory and subscription tools are clearly separated.

Naming Consistency4/5

Most tools follow a verb_noun pattern (e.g., search_jobs, validate_claim, subscribe). However, some tools like pipeworx_feedback, pipeworx_trending, entity_profile deviate with noun_noun or proper noun patterns, creating minor inconsistency.

Tool Count4/5

29 tools is above the typical 3-15 range, but the server covers a wide breadth of domains (jobs, company data, betting, memory, monitoring) so each tool earns its place. Slightly over-scoped but reasonable.

Completeness3/5

The tool set covers many domains but has notable gaps. For jobs, only search/list/get exist (no create/update/delete). For company data, update is missing. For betting, there is analysis but no placement. The set is broad but not deeply complete for any single domain.

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