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H1b

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

A4.6/5.0
Behavior5/5

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

Annotations already mark the tool as readOnly and idempotent. The description adds significant behavioral details: response structure (tracked, expired, snapshot_dates), limits (60-day TTL, decay from daily closes), and snapshot gaps. No contradiction with annotations.

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 well-structured with clear sections (purpose, args, response, limits) and front-loaded. It is slightly verbose but every sentence adds value, so it earns its length.

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?

Despite no output schema, the description thoroughly explains the response format (tracked[], expired[], snapshot_dates[]) and all relevant fields. It also covers limitations, making it complete for the tool's complexity.

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 covers 100% of parameters with descriptions. The description adds default values (days=14, window='1wk'), constraints, and explains 'window' as snapshot family. This enriches understanding 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 clearly states the tool's purpose: providing edge persistence and decay telemetry from daily polymarket_edges snapshots. It explicitly answers 'how long has this edge existed and is it shrinking?' and distinguishes itself from the sibling tool polymarket_edges by focusing on time-series trends rather than static snapshots.

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 a usage context with the example of fresh vs. old edges, implying when to use the tool. However, it does not explicitly name alternative tools or provide direct comparison, so some guidance is left implicit.

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

Several overlapping clusters exist: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research all handle research questions, polymarket_edges and polymarket_arbitrage both scan for trading opportunities, and discover_tools/suggest_questions serve similar discovery purposes. The long, use-case-specific descriptions help, but an agent could still easily pick the wrong tool among these near-duplicates.

Naming Consistency4/5

Most tools follow a snake_case verb_noun or noun pattern (resolve_entity, validate_claim, list_subscriptions, h1b_salary), which is fairly consistent. However, there are deviations: bare verbs like recall/remember/forget/subscribe, noun-only phrases like entity_profile and recent_changes, and the ask_pipeworx_beta suffix variant break the pattern slightly.

Tool Count2/5

34 tools is well past the 25+ threshold, and the server is named 'H1b' while only 3 of the 34 tools relate to H-1B data. The rest is a sprawling mix of data research, prediction-market analytics, memory, subscriptions, AI visibility checks, and unrelated utilities like generate_llms_txt and scan_dependency — a severe scope mismatch.

Completeness4/5

As a de facto Pipeworx research platform, the surface is nearly complete: open-ended queries, grounded evidence mode, deep multi-source research, entity resolution, comparison, claim validation, subscriptions, memory, and feedback. The H-1B sub-domain covers employer, salary, and top-sponsor lookups, though the mention of green cards is a small mismatch since only LCA data is provided.

Resources