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Chaos Index

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

A4.5/5.0
Behavior5/5

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

Annotations declare readOnly, idempotent, openWorld, non-destructive. Description adds rich behavioral context: data from daily snapshots with 60-day TTL, decay on daily closes (not intraday), response structure details (tracked, expired, snapshot_dates), and limitations. No contradiction 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Though lengthy, every sentence provides value: purpose, use case, response fields, limitations, and computation details. Front-loaded with core question. No fluff.

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 description fully explains return values: tracked[], expired[], snapshot_dates[] with field details. Covers data source, TTL, start date, and computation method. Complete for a complex temporal analysis tool.

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% with full descriptions for both parameters. description basically repeats schema info ('days lookback default 14 max 30' vs schema 'default 14 clamp 2-30'; window lists same values). Adds no extra meaning beyond schema, so baseline 3.

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?

Description clearly states verb+resource: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots.' It answers specific questions about edge duration and decay, distinguishing from sibling tools like polymarket_edges (raw data) and polymarket_arbitrage (different analysis).

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?

Description gives clear context: 'a fresh wide edge and a 3-week-old wide edge are different trades' – guides when to use for trade decision-making. Lacks explicit alternatives or when-not-to-use, but the sibling list provides contrast implicitly.

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
Disambiguation2/5

The set contains multiple near-duplicate entry points: ask_pipeworx, ask_pipeworx_beta (explicitly identical right now), and ask_pipeworx_grounded overlap heavily, while deep_research, discover_tools, and suggest_questions all claim to be the 'call this first' tool. Also, ai_visibility_check vs scan_competitor_ai_presence and the five polymarket_* tools create boundaries an agent could easily mischoose.

Naming Consistency4/5

Most tools follow a clear snake_case verb_noun pattern (compare_entities, validate_claim, unsubscribe, search_within). Minor deviations exist — chaos_index_calculate puts the verb last, entity_profile is noun-only, and the pipeworx_ prefix is applied inconsistently (pipeworx_trending vs ask_pipeworx) — but the overall style is predictable.

Tool Count2/5

32 tools is too many for a coherent single-purpose server; the set spans data routing, prediction markets, subscriptions, memory, AI visibility, npm scanning, and llms.txt generation. It reads as a bundled suite of unrelated utilities rather than a focused tool surface.

Completeness3/5

Subdomains are individually fairly complete: subscription CRUD, memory CRUD, and the prediction-market workflow (research, edges, arbitrage, fill risk, tracking) are all covered. However, the overall domain is incoherent, and gaps exist such as no update-subscription operation and no way to modify an existing memory beyond overwriting via remember.

Resources