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Duration

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. First observed

TDQS

A4.4/5.0
Behavior5/5

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

The description discloses extensive behavioral details: response structure with tracked, expired, and snapshot_dates arrays, interpretation of fields like trend and decay_pp_per_day, and limitations like snapshot gaps and TTL. Annotations already confirm read-only and idempotent, and the description adds significant context without contradiction.

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 but well-organized: purpose, arguments, response, limitations. It is front-loaded with the core question. While dense, every sentence adds value, though it could be slightly tightened.

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 the tool's complexity (time-series analysis, multiple response arrays, no output schema), the description covers all necessary information: response fields, interpretation, limitations, and data sources. It fully compensates for the lack of an output schema.

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%. The description restates the parameters (days, window) with defaults and constraints, adding minimal context about their effect on snapshot range. Baseline 3 is appropriate as schema already documents them fully.

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 provides 'edge persistence and decay telemetry' from daily snapshots, answering 'how long has this edge existed and is it shrinking?'. It distinguishes from the sibling 'polymarket_edges' by focusing on time-series behavior 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 explains the use case: differentiating fresh vs. stale edges, implying when to use this tool (for historical analysis) vs. alternatives. It mentions limits like 60-day TTL but doesn't explicitly exclude other scenarios.

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
Disambiguation3/5

The tool set includes several overlapping tools: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded serve very similar purposes with only subtle differences in behavior (beta/grounded). Additionally, deep_research and validate_claim partially overlap with these. The prediction market tools are numerous but distinct, and memory/duration tools are clear. Overall, an agent would face some confusion when selecting among the ask_pipeworx variants.

Naming Consistency4/5

Most tools use consistent underscore_case with descriptive verb_noun patterns (e.g., list_subscriptions, resolve_entity, validate_claim). A few memory and subscription tools are single-word verbs (forget, recall, remember, subscribe, unsubscribe), which deviates slightly but remains readable. Overall naming is predictable and clear.

Tool Count3/5

With 33 tools, the server is on the heavier side. The server covers a broad scope (data queries, prediction markets, AI visibility, memory, utilities, subscriptions), which justifies many tools, but some feel redundant (e.g., three variants of ask_pipeworx, multiple polymarket edge tools). A more focused set (around 20-25) would be more typical for coherence.

Completeness4/5

The tool surface is extensive, covering factual Q&A via a universal router, entity profiles, comparisons, prediction market analysis, memory, subscriptions, AI visibility, and utilities. The meta-tool ask_pipeworx provides access to thousands of structured sources, so most data needs are addressable. Minor gaps include no direct tool for specific SEC filing retrieval beyond the meta-router, but this is covered indirectly.

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