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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).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added
  2. Removed
  3. Added

TDQS

A4.5/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint), the description details snapshot lifecycle (cache-miss gaps, 60-day TTL), return structure (tracked/expired/snapshot_dates), and computation details (daily closes, not intraday), with no contradictions to annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

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

Front-loads purpose and includes structured response details, but is verbose with lengthy inline explanations of return fields (tracked, expired, snapshot_dates) that could be more compact.

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?

Completely compensates for lack of output schema by fully describing all return fields and edge cases (gaps, TTL limits, computational basis), making the tool fully understandable for invocation.

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?

Adds value beyond schema by specifying default values (days=14, window='1wk') and constraints (days max 30, clamp 2-30), enriching the agent's understanding of parameter behavior.

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 uses specific verbs ('edge persistence and decay telemetry') and resources ('daily polymarket_edges snapshots'), clearly distinguishing this tool from the sibling 'polymarket_edges' by focusing on time-series behavior rather than current values.

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 clear context for when to use (to understand edge longevity and decay), but does not explicitly state when to avoid or list alternatives, though the purpose implies differentiation from '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

A3.8/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx and ask_pipeworx_beta are described as currently identical, and deep_research/ask_pipeworx plus the many polymarket_* tools have adjacent intents that are easy to confuse. scan_competitor_ai_presence also directly wraps ai_visibility_check, so an agent can easily select the wrong tool for the same task.

Naming Consistency3/5

Names are consistently snake_case, but the convention is mixed: some are verb-first (query_subgraph, introspect_schema, validate_claim), some are noun-first (entity_profile, polymarket_edges, pipeworx_trending), and some are bare verbs (remember, recall, forget). The repeated prefixes help, but there is no single predictable naming pattern across the set.

Tool Count2/5

With 33 tools, this exceeds a reasonable single-server footprint, and the scope sprawls across data routing, prediction markets, AI visibility, npm dependencies, llms.txt generation, memory, and subscriptions. Many tools feel like separate mini-applications rather than one coherent toolkit for The Graph.

Completeness3/5

The Pipeworx data-research and prediction-market side is well covered with lookup, grounded answers, research, comparison, profiles, claims, subscriptions, and memory. However, the server's apparent namesake, The Graph, is thin: only query_subgraph and introspect_schema exist, with no subgraph discovery, status, or management tools.

Resources