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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).

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already indicate read-only, open-world, idempotent, non-destructive. The description goes far beyond by detailing the response structure (tracked[], expired[], snapshot_dates[]), explaining snapshot gaps due to cache-miss behavior, and disclosing limitations (60-day TTL, decay computed from daily closes not intraday). 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-structured with labeled sections (ARGS, RESPONSE, LIMITS). Every sentence provides meaningful information—no filler. It is longer than typical, but for a complex telemetry tool with detailed output, the length is justified and readability is enhanced by clear segmentation.

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?

Given there is no output schema, the description compensates by defining every response field and its semantics, including edge_pp_net time-series, trend values, decay calculations, and snapshot_dates. It also outlines important limitations (history depth, TTL, non-intraday decay). This is a complete and self-contained reference for an agent.

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 both params having descriptions. The description adds minimal extra nuance (e.g., 'window (snapshot family, default 1wk)') and slightly less precise on days (says max 30 vs schema clamp 2-30). Since the schema already documents parameters thoroughly, the description's contribution is marginal, so a baseline 3 is appropriate.

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 'Edge persistence and decay telemetry' and answers a specific question: 'how long has this edge existed and is it shrinking?' It differentiates from sibling polymarket_edges by focusing on historical snapshots and decay trends rather than current edges. The verb 'tracker' plus explicit resource ('daily polymarket_edges snapshots') makes the purpose unambiguous.

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 strong context on when to use: to evaluate edge persistence/decay and understand competitive lifespan. It also explains data generation (snapshots written on cache-miss), which informs users about gaps. However, it does not explicitly name alternative tools or state when not to use it, leaving some implicit differentiation.

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

A3.6/5.0
Disambiguation2/5

Several tools have nearly identical or heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical, and the six Polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread, bet_research) all involve finding/analyzing prediction-market opportunities. deep_research and ask_pipeworx also overlap as general query routers, and ai_visibility_check vs scan_competitor_ai_presence is another confusable pair.

Naming Consistency3/5

All names use snake_case and are descriptive, but verb placement is inconsistent: some are verb-first (check_domain, compare_entities, resolve_entity), others are verb-last or noun-like (ai_visibility_check, entity_profile, pipeworx_trending, bet_research). There is no chaotic camelCase mix, but the pattern is not predictable enough to guess a tool's behavior from its name.

Tool Count2/5

33 tools is far above the typical well-scoped range and the set spans multiple unrelated domains (data lookup, prediction markets, AI visibility, memory, subscriptions, email/domain validation) that have no cohesive purpose under the 'disify' name. Most tools are not related to domain or email checking, making the count feel like a grab bag rather than a focused toolkit.

Completeness2/5

For a server named 'disify', the core domain-validation surface is minimal (only check_domain and validate_email) and misses obvious operations like WHOIS lookup or breach/debounce checks. As a general data toolset it is broad but shallow in each area, with gaps such as entity_profile only supporting US public companies and no update/delete operations for most data resources.

Resources