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Rdap

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. Added

TDQS

A4/5.0
Behavior4/5

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

Annotations already indicate read-only and non-destructive. The description adds significant details: response structure (tracked, expired, snapshot_dates), meaning of decay trend categories, limitations (60-day TTL, daily closes only). 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.

Conciseness3/5

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

Well-organized but verbose. The information is packed into a single paragraph; could be split for readability. Every sentence adds value, but length may slow scanning.

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?

Extremely thorough given no output schema. Covers return format, trend computation, edge cases (expired opportunities, snapshot gaps), and limitations. Leaves little ambiguity for an agent.

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 both parameters with descriptions. The description adds defaults and clamping behavior for 'days', and explains 'window' families. Provides extra context beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it provides edge persistence and decay telemetry from daily snapshots, answering the question of how long an edge has existed. The purpose is specific and distinct from siblings, though no explicit differentiation is given.

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?

Describes the scenario where a fresh wide edge differs from an older one, guiding when to use the tool. Includes limits on lookback and snapshot history. Lacks explicit alternatives or when-not-to-use statements.

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

B3.1/5.0
Disambiguation2/5

The RDAP tools (domain, ip, asn, nameserver, entity) are distinct, but the larger Pipeworx collection creates significant overlap: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicates, and the polymarket_* family (edges, arbitrage, edge_tracker, fill_risk, kalshi_spread) has heavily overlapping purposes. ai_visibility_check and scan_competitor_ai_presence also overlap.

Naming Consistency3/5

Most tools use snake_case with a descriptive verb_noun pattern (e.g., ask_pipeworx, discover_tools, recent_changes), but the set mixes short single nouns (domain, ip, asn, entity) with compound names, and there are oddities like generate_llms_txt and ask_pipeworx_grounded that deviate from a clear uniform convention.

Tool Count2/5

At 36 tools, the server is far heavier than expected for an RDAP service. The RDAP core only needs ~5 tools; the rest are an unrelated Pipeworx mega-suite (data queries, prediction markets, memory, subscriptions, feedback) that makes the server feel like a dumping ground rather than a focused toolset.

Completeness4/5

For the RDAP portion, coverage is complete: domain, IP, ASN, nameserver, and entity records are all present. The broader Pipeworx features also cover their own workflows (query, research, comparison, memory CRUD, subscription lifecycle), but the server's stated RDAP identity is muddied by these extras, and some integration points (e.g., no direct update for RDAP data, which is read-only anyway) are inherent limitations.

Resources