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Complex Portal

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

A4.3/5.0
Behavior5/5

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

The description goes well beyond annotations, which already indicate readOnly, openWorld, idempotent. It details the response structure (tracked[], expired[], snapshot_dates[]), explains decay computation (daily closes of edge_pp_net, net of slippage, not intraday), and notes history bounds (60-day TTL, from snapshot enablement). No contradictions 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.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is informative but on the longer side, with multiple paragraphs explaining response fields and limits. While well-structured, it could be more concise by consolidating some details. It is front-loaded with the core purpose, which is good.

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 no output schema, the description compensates by explaining the response structure in detail (tracked[], expired[], snapshot_dates[] with field meanings). It covers limitations (TTL, snapshot enablement) and nuances (decay numbers from daily closes, not intraday). This makes the tool fully understandable without additional documentation.

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%, so the description adds marginal value. It restates defaults and clamps (days: default 14, max 30; window: default '1wk') which are already in the schema. The description includes 'snapshot family' for window, but this is not a significant addition beyond the schema's description.

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 answers the specific question 'how long has this edge existed and is it shrinking?' and distinguishes itself from sibling tools by focusing on time-series analysis rather than raw data. The verb 'tracker' and resource 'polymarket_edges snapshots' are explicit.

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 implies when to use this tool: when you need to assess edge persistence over time, noting that a fresh wide edge and an old wide edge are different trades. It does not explicitly list alternatives or when not to use it, but the context is clear enough for an AI agent to infer appropriate usage.

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

A3.8/5.0
Disambiguation2/5

Multiple tools have overlapping boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical, ask_pipeworx_grounded and deep_research heavily overlap with them, and entity_profile, compare_entities, recent_changes, and validate_claim all circle the same company-data space. The prediction-market cluster (polymarket_edges, polymarket_edge_tracker, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread) also requires careful reading to distinguish. The descriptions help, but the set relies on them to disambiguate near-duplicates.

Naming Consistency4/5

Nearly all tools follow a consistent lowercase snake_case style, whether verb_noun (search_complexes, validate_claim, unsubscribe), noun_verb (entity_profile, recent_changes), or brand-like (ask_pipeworx, polymarket_edges). There is no camelCase mixing or chaotic verb style. Minor inconsistency exists between imperative verbs (remember, forget, subscribe) and noun-style names, but the overall pattern is readable and predictable.

Tool Count2/5

33 tools is well above the 25-tool 'heavy' threshold, and many are meta-tools (discover_tools, suggest_questions, pipeworx_feedback, pipeworx_trending, remember/recall/forget) that pad the surface. The server is named 'Complex Portal', yet only two tools serve that purpose—the rest belong to a broad data platform. The count feels inflated and misaligned with the server's stated identity.

Completeness3/5

For the broad Pipeworx data platform the surface is fairly complete: universal querying, grounded answers, deep research, entity profiles, comparisons, claim validation, subscriptions, memory, and feedback. For the Complex Portal domain named by the server, coverage is thin—just search and fetch-by-accession, with no browsing, species filtering, or cross-reference tools. The core workflow works, but the namesake domain is under-served relative to the rest of the set.

Resources