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Arcgis Albuquerque

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

A4.4/5.0
Behavior5/5

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

Beyond the annotations (read-only, idempotent, non-destructive), the description discloses critical behavioral details: history depth bounded by a 60-day snapshot TTL, gaps due to cache-miss writes, decay computed from daily closes not intraday, and the exact semantics of expired[] and snapshot_dates[]. This goes well beyond what annotations convey.

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 well-structured with clear ARGS, RESPONSE, and LIMITS sections. Every sentence provides value, explaining response fields and limitations. It could be slightly trimmed, but the labeled structure makes it easy to scan.

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 lack of an output schema, the description fully specifies the three response arrays (tracked, expired, snapshot_dates), their fields, and interpretation of decay and lifespan. It also explains the underlying data pipeline (snapshots written on cache-miss) and limitations, making the tool's behavior comprehensively understandable.

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?

Both parameters are fully described in the schema with defaults and ranges (days: default 14, clamp 2-30; window: enum 24hr|1wk|1mo). The description repeats 'lookback, default 14, max 30' and 'snapshot family, default 1wk' but adds no substantive new meaning beyond the schema, hence baseline 3 for high schema coverage.

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: tracking edge persistence and decay telemetry from daily polymarket_edges snapshots. It answers the specific question 'how long has this edge existed and is it shrinking?', which distinguishes it from sibling tools like polymarket_edges (current edges) and polymarket_arbitrage (arbitrage detection).

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 context on when to use it by contrasting fresh vs 3-week-old edges, implying this tool is for assessing edge age and decay. It doesn't explicitly name alternatives, but the mention of being built from polymarket_edges snapshots and the question it answers clearly differentiate it from siblings.

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

Several tool clusters overlap heavily: ask_pipeworx and ask_pipeworx_beta are documented as functionally identical right now, and polymarket_arbitrage / polymarket_edges / polymarket_edge_tracker all surface trade opportunities with similar outputs. discover_tools and suggest_questions also cover similar 'what can I do' territory.

Naming Consistency3/5

All names are snake_case and many use verb_noun (query_layer, resolve_entity, generate_llms_txt), but a large minority use noun/adjective phrases (layer_info, recent_changes, polymarket_edges, bet_research) or bare verbs (remember, forget, recall). The pattern is readable but not fully predictable.

Tool Count2/5

34 tools is over the 25-tool threshold and deeply mismatched with the server name: only 3 of them (search_datasets, query_layer, layer_info) relate to ArcGIS Albuquerque. Most of the surface is a general-purpose Pipeworx data platform, making the set bloated and unfocused.

Completeness2/5

The advertised ArcGIS domain has only search-schema-query coverage: there is no way to list all datasets, apply spatial filters, or get service-level metadata. The Pipeworx half is feature-rich, but for the server as titled the tool surface has significant gaps and a large amount of irrelevant functionality.

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