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Nba

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?

The description extensively discloses behaviors: history bound by 60-day TTL, snapshot gaps due to cache misses, decay calculated from daily closes (not intraday), and detailed response structure. This adds significant context beyond the annotations (readOnly, idempotent, openWorld), which already indicate safety. 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 purpose and each subsequent sentence adds value, but it is somewhat verbose with detailed response fields. It could be slightly more concise without losing clarity, but overall well-structured for a complex tool.

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 and lack of output schema, the description fully details the response arrays (tracked, expired, snapshot_dates) with field explanations and limitations (60-day TTL, cache-miss gaps, intraday vs daily close). It is complete for agent invocation.

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 descriptions already explaining defaults and clamping for days, and the window family for window. The description reiterates defaults and lists example values but does not add substantial new meaning beyond the schema, earning the baseline 3.

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 ('how long has this edge existed and is it shrinking?') and distinguishes itself from siblings like polymarket_edges by focusing on historical tracking 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 implies usage context by contrasting fresh wide edges with old wide edges, highlighting the tool's value for assessing edge decay. However, it does not explicitly state when to use this tool over alternatives (e.g., polymarket_edges) or provide conditions to avoid, so some guidance is left implicit.

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

A3.5/5.0
Disambiguation2/5

Many tools serve overlapping purposes: ask_pipeworx, ask_pipeworx_grounded, deep_research all provide factual lookups with similar boundaries. NBA-specific tools are mixed with generic Pipeworx utilities, making it hard for an agent to distinguish when to use which.

Naming Consistency2/5

Tool names mix conventions: snake_case (ai_visibility_check), camelCase (generate_llms_txt), and inconsistent verb usage (ask_pipeworx vs. search_players). No clear pattern emerges across the set.

Tool Count2/5

With 34 tools, the server is overpopulated, especially given its 'Nba' focus. Many tools (e.g., remember, forget, list_subscriptions) are generic utilities unrelated to NBA data, inflating the count beyond a coherent scope.

Completeness2/5

For an NBA server, tools are severely incomplete: missing player stats, team stats, season standings, and live game details. While the Pipeworx data tools are extensive, they diverge from the server's apparent purpose, leaving clear gaps.

Resources