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Edgar

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. Added
  2. Removed
  3. Added

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already mark it as read-only, idempotent, and non-destructive. The description adds extensive behavioral detail: it reads from snapshots, uses daily closes, computes trends and decay, and explains response structure. 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 fairly long but well-organized: purpose, parameters, response format with examples, and limitations. Every sentence adds value, though some technical jargon could be simplified. Front-loading is good.

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 exists, so the description comprehensively explains return values (tracked, expired, snapshot_dates) and their fields. It also covers limits and edge cases (gaps in snapshots). Complete for a tool with 2 optional params.

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 clear descriptions for both parameters (days, window). The description adds default values and clamps but does not significantly extend understanding beyond schema. 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?

The description clearly states the tool's purpose: providing edge persistence and decay telemetry from daily snapshots. It answers a specific question about edge age and trend, distinguishing it from sibling tools like polymarket_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 gives context by contrasting fresh vs. old edges, implying when to use this tool for historical insight. It also notes limits like 60-day TTL, but does not explicitly compare to alternatives or state when not to use.

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

A4.3/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose, with detailed descriptions that prevent confusion. Overlapping tools like ask_pipeworx vs ask_pipeworx_grounded are explicitly differentiated by use case (casual vs high-stakes) and refusal behavior.

Naming Consistency4/5

Names are mostly consistent using lowercase underscores, but there is a mix of verb-initial (ask_pipeworx, generate_llms_txt) and noun-initial (edgar_company_facts, polymarket_arbitrage) patterns, which slightly reduces predictability.

Tool Count3/5

37 tools is high, but the server covers a broad domain of authoritative data sources (SEC, FDA, FRED, prediction markets, etc.). While it exceeds the typical 15-tool threshold, each tool serves a distinct data need and the count feels justified for the scope.

Completeness5/5

The tool surface covers the full lifecycle of data retrieval and analysis: discovery (discover_tools), single queries (ask_pipeworx), grounded lookups (ask_pipeworx_grounded), multi-source research (deep_research), entity profiles, comparisons, historical data, and subscription monitoring. No major gaps are evident.

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