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words

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

A4.6/5.0
Behavior5/5

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

Beyond the annotations (readOnly, idempotent), the description discloses key behavioral traits: history depth is bounded by a 60-day snapshot TTL, snapshots are written on cache-miss so gaps mean no scan, and decay is computed from daily closes of edge_pp_net. This adds rich context about data freshness, limitations, and computation method.

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 long but every sentence carries information. It is well-structured with labels like 'RESPONSE:' and 'LIMITS:' to separate concepts. It avoids redundancy and justifies its length by covering multiple aspects (purpose, response schema, data caveats) in a compact, organized manner.

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?

Despite lacking an output schema, the description fully details the response format (tracked[], expired[], snapshot_dates[]) and explains each field. It also covers limitations, default parameter behavior, and how to interpret gaps. This is complete for a read-only telemetry tool with 2 optional parameters.

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?

The input schema is already descriptive (100% coverage), but the description adds value by explaining the default lookback (14, max 30) and the window family (default '1wk'), and by linking parameters to the response structure (e.g., snapshot_dates reflecting days with data). This exceeds the schema's baseline.

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: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots.' It answers a specific question ('how long has this edge existed and is it shrinking?') and distinguishes itself from sibling tools like polymarket_edges by focusing on edge history rather than current edge 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?

The description provides clear context on when to use this tool (to assess edge decay/persistence) and includes exclusions like 'not intraday' (based on daily closes). It implies alternatives (polymarket_edges for current snapshots) but does not explicitly name them or provide a when-not-to-use list for each sibling. Overall, usage guidance is strong but could be more explicit.

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

A3.7/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx_beta is currently identical to ask_pipeworx, and find_related with syn/rhy options duplicates find_synonyms and find_rhymes. The six Polymarket tools and four entity-research tools also have fuzzy boundaries, so agents will struggle to reliably pick the right one.

Naming Consistency2/5

Conventions are mixed: most tools follow verb_noun (ask_, find_, compare_, validate_) but several are bare nouns or noun phrases (entity_profile, recent_alertes, recent_changes) and others are bare verbs (remember, forget, subscribe, unsubscribe). The server name 'words' matches only five of 36 tools, adding further confusion about what to expect here.

Tool Count2/5

36 tools is well into the heavy range, and the set bundles word lookups, a universal data router, six Polymarket tools, memory, subscriptions, and meta-utilities under one server. Many of these would be better split into dedicated, purpose-scoped servers.

Completeness4/5

For the dominant data-research and prediction-market scope, the surface is strong: grounded lookups, deep reseach, entity profiles, compareions, change feeds, claim verification, edge scanners, fill-risk checks, subscriptions, and memory are all covered. Minor gaps exist: taking 'words' literally there is no defintion or spelling tool, and there is no generic open-web search, but as a data-research toolkit it is quite complete.

Resources