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Odoo

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
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds valuable behavioral details beyond annotations: snapshot TTL (60 days), decay computation from daily closes (not intraday), and the structure of the response (tracked, expired, snapshot_dates). 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 informative and front-loaded with the core question. It uses clear structure (RESPONSE sections) and every sentence adds unique value. Slightly dense but justified by the complexity of the telemetry data provided.

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?

No output schema exists, so the description fully explains return fields: tracked[], expired[], snapshot_dates[]. It also covers limits (60-day TTL, daily closes). For a tool with two optional parameters and no output schema, the description is comprehensive and leaves no critical gaps.

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 both parameters described. The description reinforces parameter roles (lookback days, window family) and adds context about defaults and limits (days default 14, clamp 2-30; window options). Slight redundancy but adds clarity beyond 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 clearly identifies the tool's purpose: providing edge persistence and decay telemetry from daily snapshots. It answers specific questions about edge longevity and trend, and distinguishes itself from the sibling 'polymarket_edges' tool by focusing on time-series analysis rather than raw snapshot data.

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 implicitly guides usage by contrasting fresh vs. old edges and mentioning that a three-week-old wide edge is different. It doesn't explicitly list alternatives or when-not-to-use scenarios, but the context is sufficient for an agent to select this tool for edge persistence analysis.

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

A3.7/5.0
Disambiguation3/5

Some tools have overlapping purposes (e.g., ask_pipeworx and ask_pipeworx_grounded; multiple Polymarket analysis tools) but descriptions help differentiate. Odoo-specific tools are distinct among themselves but muddled with many general-purpose tools.

Naming Consistency2/5

Naming is heavily inconsistent: Odoo tools follow 'odoo_list_*' pattern while others use varied structures like verb-first (forget, remember) or noun-first (entity_profile, recent_alerts). No unified verb_noun pattern across the set.

Tool Count3/5

At 32 tools, the server exceeds the typical well-scoped range (3–15) but is not excessively large. Many tools are generic and could be trimmed, making the set feel heavier than necessary.

Completeness2/5

Despite many tools, the server lacks essential CRUD operations for Odoo (e.g., create/update/delete leads) and the 'Odoo' name misrepresents the actual focus. Gaps in Odoo functionality and mixed domains create a sense of incompleteness.

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