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Statbank Md

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).

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, but the description adds substantial behavioral context: snapshots written on cache-miss, gaps meaning no scan, 60-day TTL bound, decay computed from daily closes not intraday, and detailed response semantics. This goes well beyond the annotation baseline.

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

Conciseness5/5

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

The description is well-structured with ARGS, RESPONSE, and LIMITS sections. It is dense but every sentence adds value, and the purpose is front-loaded. No wasted words.

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?

With no output schema, the description fully compensates by explaining the response structure (tracked[], expired[], snapshot_dates[]) and field semantics, plus limitations like TTL and data gaps. Complete for a read-only telemetry tool with two optional parameters.

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 detailed descriptions for both parameters (days, window). The description restates defaults ('default 14, max 30', 'default 1wk') but does not add meaningfully beyond the 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 function with a specific verb and resource: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots.' It distinguishes itself from sibling tools like polymarket_edges by focusing on historical persistence and decay 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 provides clear context for when to use the tool ('how long has this edge existed and is it shrinking?') and contrasts fresh versus old edges. However, it does not explicitly name alternative tools or state when not to use it, though this is implied by the sibling tool list.

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

A3.6/5.0
Disambiguation2/5

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical in scope, and the prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk) share similar functions. While some tools are distinct, the boundaries between many are unclear.

Naming Consistency2/5

Names mix product-like identifiers (ask_pipeworx, deep_research), descriptive nouns (entity_profile, subjects), and inconsistent verb forms (query_table, resolve_entity, scan_competitor_ai_presence). No consistent verb_noun pattern is maintained across the set.

Tool Count2/5

With 34 tools, the server is overpopulated relative to its apparent purpose. The name 'Statbank Md' suggests a narrow statistical service, but only 3 tools are Statbank-specific; the rest form a sprawling general-purpose data toolkit. The count is far beyond what the core function needs.

Completeness3/5

For the Statbank subset, the surface is complete (browse subjects, get metadata, query data). As a general data research suite, it covers many domains (SEC, FDA, economics, prediction markets) but lacks execution/trading tools for prediction markets and has no bulk data export or analytics beyond excerpts. Notable gaps exist but many core workflows are covered.

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