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Statec Lu

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?

The description goes far beyond the annotations (readOnlyHint, idempotentHint, etc.) by disclosing specific behaviors: the response structure (tracked[], expired[], snapshot_dates[]), the 60-day snapshot TTL limit, daily closes for decay computation, and why snapshot gaps occur. This provides rich behavioral context not available from annotations.

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?

Although the description is long, it is extremely dense and well-structured, with clear sections for Args, RESPONSE, and LIMITS. Every sentence adds informative value, and the purpose is front-loaded. No fluff or redundancy is present.

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 the absence of an output schema, the description fully explains the return values (tracked[], expired[], snapshot_dates[] with their fields) and the operational constraints (TTL, snapshot frequency, decay methodology). It covers all essential context needed for correct usage.

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?

The input schema already provides 100% coverage of both parameters (days and window) with descriptions including defaults and ranges. The description adds minimal extra meaning, merely restating 'default 14', 'max 30', and 'default 1wk' already present in the schema. It does not provide additional parameter semantics beyond what the schema already offers.

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' and explicitly answers the question 'how long has this edge existed and is it shrinking?'. It distinguishes itself from sibling tool polymarket_edges by focusing on temporal analysis of edge snapshots rather than just 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 for when to use the tool: to assess whether an edge is persistent or decaying, emphasizing that 'a fresh wide edge and a 3-week-old wide edge are different trades'. However, it does not explicitly mention alternatives or when not to use it, only implying that it analyzes historical snapshots.

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

Multiple tools have blurry boundaries: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical (beta is explicitly 'exactly' the stable version), and ai_visibility_check vs scan_competitor_ai_presence is a single-vs-batch duplicate. The six polymarket_* tools are differentiated by long descriptions, but their overlapping concerns (edges, arbitrage, fill risk, spread) would frequently misroute an agent, and discover_tools vs suggest_questions also compete.

Naming Consistency3/5

Names follow two coexisting conventions: verb_noun for actions (get_data, resolve_entity, validate_claim) and domain-prefixed families (polymarket_*, pipeworx_*, ask_pipeworx_*). Within each family the pattern is consistent, but mixing the two styles across the set, plus outliers like generate_llms_txt and bare verbs (remember, forget, recall), makes the overall scheme feel uneven though still readable.

Tool Count2/5

34 tools is well past the 'heavy' threshold and the count is not justified by the server's stated identity: a server named 'Statec Lu' (Luxembourg statistics) contains only 3 STATEC tools buried among general data-platform, prediction-market, AI-visibility, npm-scanning, and memory utilities. The sprawling, multi-domain surface would be more coherent split into separate servers.

Completeness3/5

The STATEC subset is complete (list_dataflows → dataflow_structure → get_data forms a full browse/fetch lifecycle), and the broader research surface covers entity resolution, grounded answers, comparison, claim verification, and subscription/alert/memory management. However, the overall domain is incoherent—a STATEC server missing nothing for statistics but carrying 31 unrelated tools—and there are notable gaps such as no tool to directly fetch a pipeworx:// citation URI and no execution side for the extensive Polymarket analysis tools.

Resources