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Hpo Api

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 indicate read-only, idempotent, non-destructive. Description adds crucial limitations: 60-day snapshot TTL, snapshot availability from enablement date, decay computed from daily closes not intraday. Also explains the meaning of trend and decay fields, going well beyond 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?

Front-loaded with the core question, then breaks down response structure with bullet-like clarity, and ends with limitations. Every sentence adds value; no fluff. Despite length, it is efficient and well-organized.

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, yet the description fully covers return fields: tracked[] with full time-series, first_seen, trend, decay_pp_per_day; expired[] with lifespan_days; and snapshot_dates[]. Also explains decay computation and limitations. Complete for agent 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?

Schema coverage is 100% for both parameters. Description repeats the defaults and helps clarify the window parameter, but adds minimal new meaning beyond the schema's 'lookback in days' and 'which window family'. 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 purpose: 'Edge persistence and decay telemetry' answering 'how long has this edge existed and is it shrinking?'. It explicitly distinguishes from sibling tools like polymarket_edges by focusing on historical time-series analysis rather than current state.

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?

Provides clear context on when to use: 'a fresh wide edge and a 3-week-old wide edge are different trades'. It explains the response structure but does not explicitly mention when not to use it or specify alternatives. Could improve by contrasting with raw edge data tool.

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

A4.1/5.0
Disambiguation3/5

Many tools have distinct purposes, but there is overlap among the Polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_kalshi_spread) and between ai_visibility_check and scan_competitor_ai_presence. Detailed descriptions help, but some tools could still be confused.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (e.g., ai_visibility_check, resolve_entity, validate_claim). No mixing of conventions.

Tool Count2/5

With 27 tools spanning HPO, data queries, betting, memory, and utilities, the server is over-scoped. It aggregates multiple domains that would be better split into separate servers. The count feels excessive for a coherent set.

Completeness4/5

Within each domain (HPO ontology, Pipeworx data, Polymarket betting, etc.), the tool surface is reasonably complete. However, the overall server lacks a single clear purpose, making it hard to assess completeness holistically.

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