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Arcgis Orovalley

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. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations declare read-only/idempotent, but the description adds critical behavioral context: snapshots are written only on cache-miss (gaps mean no scan), a 60-day TTL bounds history, and decay is computed from daily closes, not intraday. This goes well beyond the 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?

The description is dense but well-organized into Args, RESPONSE, and LIMITS sections. Every sentence delivers useful information, and the structured format 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?

With no output schema, the description fully explains the response format: tracked[] with time-series, first_seen, trend, decay; expired[] with lifespan; snapshot_dates[]. It also covers limitations (TTL, snapshot gaps, daily closes). This is complete for a complex telemetry tool.

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?

Schema coverage is 100% with good parameter descriptions. The description reinforces defaults but adds semantic nuance: 'window (snapshot family)' clarifies that the parameter selects a family of snapshots, not just a time window. This adds value beyond the schema.

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 opens with a specific verb and resource: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots.' It clearly distinguishes itself from siblings like polymarket_edges by focusing on time-series behavior ('how long has this edge existed and is it shrinking?').

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 gives clear context for when to use this tool: to evaluate edge persistence/decay, with a concrete example of fresh vs. old wide edges. It does not explicitly name alternatives or exclusions, but the context makes the use case clear.

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

A3.9/5.0
Disambiguation3/5

The descriptions are extraordinarily detailed and genuinely differentiate most tools, but the set contains near-clones (ask_pipeworx, ask_pipeworx_beta which is explicitly 'identical' to it, and ask_pipeworx_grounded) plus five polymarket tools whose boundaries (arbitrage vs edges vs edge_tracker vs fill_risk vs kalshi_spread) overlap enough to cause misselection. An agent navigating this surface must read full descriptions to choose correctly, which defeats quick tool selection.

Naming Consistency3/5

Most tools follow a snake_case verb_noun pattern (ask_pipeworx, compare_entities, resolve_entity), but there are clear deviations: bare single-word verbs (remember, recall, forget, subscribe, unsubscribe), prefix-family names (pipeworx_feedback, pipeworx_trending; polymarket_edges, polymarket_fill_risk), and a disjoint ArcGIS trio (layer_info, query_layer, search_datasets) that breaks the dominant convention. It is readable but not predictable across the whole set.

Tool Count2/5

34 tools is far beyond what the 'Arcgis Orovalley' purpose warrants — only 3 tools (search_datasets, layer_info, query_layer) actually relate to GIS, with 31 unrelated tools bolted on covering financial data, prediction markets, memory, subscriptions, and AI visibility. This is a sprawling mega-server where an agent must hold an enormous option set in mind; the surface appears to be several platforms fused together rather than one well-scoped toolset.

Completeness3/5

The genuine ArcGIS surface (search datasets → layer_info → query_layer) is a complete read-only workflow with no dead ends, and the Pipeworx side is impressively comprehensive (routing, grounded answers, research, entity, compare, resolve, validate, memory, subscriptions, feedback). But the tool set as a whole serves no single coherent domain — the declared purpose (ArcGIS Oro Valley data) lacks any write/editing operations, while the majority of the surface addresses unrelated concerns, so 'complete' only applies to one small slice of the 34 tools.

Resources