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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

A4.6/5.0
Behavior5/5

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

The annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds substantial behavioral detail beyond annotations, including the response structure (tracked, expired, snapshot_dates), decay computation details, window family handling, snapshot TTL, and limitations (e.g., daily closes, not intraday). 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 front-loaded with the key question and uses a clear structure with sections for response fields. While lengthy, every sentence contributes useful information. Minor improvement would be to tighten some details.

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 no output schema, the description thoroughly explains all return fields (tracked, expired, snapshot_dates) with their semantics. It also covers limitations (snapshot TTL, decay computation) and provides enough context for the agent to understand the tool's role relative to siblings. Complete for its complexity.

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%, so the description adds value by explaining the default and max values for 'days' and the meaning of 'window' families. It also provides context about snapshot caching behavior, which is not in the schema. However, the description repeats some schema info, so it's not fully additive.

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 tracks edge persistence and decay, answering the specific question of how long an edge has existed and whether it's shrinking. It distinguishes itself from the sibling tool 'polymarket_edges' by focusing on time-series analysis rather than 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 strong context for when to use this tool (e.g., distinguishing a fresh wide edge from an old one), but does not explicitly state when not to use it or name alternatives. It gives implicit usage guidance through the contrast with 'polymarket_edges'.

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

A4.1/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but some overlap exists among the polymarket tools (e.g., bet_research, polymarket_arbitrage, polymarket_edges) and the ask_pipeworx variants. The detailed descriptions help distinguish them, but an agent might still misselect in those groups.

Naming Consistency4/5

Tool names follow a mostly consistent verb_noun pattern in snake_case. Minor deviations exist, such as 'remember' vs 'recall' and the mixed use of verbs and nouns (e.g., 'ask_pipeworx' vs 'polymarket_arbitrage'), but overall the pattern is predictable.

Tool Count3/5

With 35 tools, the server is on the heavy side. While each tool serves a specific purpose, the sheer number may be overwhelming, and some subsets (like the 7 polymarket tools) could potentially be consolidated. Still, the scope justifies many of them.

Completeness4/5

The server covers a wide range of functionalities: Python package management, company research, prediction markets, monitoring, memory, and data queries. Minor gaps exist (e.g., no direct tool for editing subscriptions), but the surface is generally comprehensive and well-rounded.

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