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Data Lv

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. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already mark it as read-only, open-world, idempotent, non-destructive. The description adds behavioral details: reads snapshots, returns time-series, trend, decay, expired opportunities, and snapshot dates. No contradiction.

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 front-loaded with the core purpose and is well-structured. It is somewhat lengthy but every sentence provides value; minor tightening could improve conciseness without losing detail.

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 return structure (tracked, expired, snapshot_dates) and covers limitations (TTL, start date, daily closes). No gaps given 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 coverage is 100% with descriptions for both parameters. The description adds default values and clamping for 'days', and explains the 'window' parameter's meaning. This adds meaning 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 states the tool provides 'edge persistence and decay telemetry' from daily snapshots, answering specific questions about edge age and trend. It distinguishes itself from sibling tools like 'polymarket_edges' by focusing on time-series and decay analysis.

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 explains when to use the tool (to assess if an edge is fresh vs. old) and implies when not to use it (intraday not supported). It does not explicitly list alternatives but context makes the complementary nature clear.

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

A4.3/5.0
Disambiguation5/5

Each tool targets a distinct purpose: data lookup (ask_pipeworx vs deep_research), entity profiles, comparisons, memory, monitoring, and prediction market analysis. Overlaps are minimal and mitigated by explicit usage guidance (e.g., ask_pipeworx vs. ask_pipeworx_grounded vs. deep_research).

Naming Consistency5/5

All tools use descriptive snake_case names following a verb_noun or verb_preposition pattern (e.g., entity_profile, resolve_entity, scan_dependency). The naming is predictable and internally consistent, making it easy for an agent to infer tool purposes.

Tool Count3/5

33 tools is on the high end for typical MCP servers. However, the server covers an exceptionally broad domain (structured data across SEC, FDA, FRED, weather, news, crypto, etc.) and includes meta-tools, monitoring, and memory. The count is justified but may feel excessive for many use cases.

Completeness5/5

The tool surface covers the full lifecycle of data access and analysis: discovery (discover_tools, suggest_questions), retrieval (ask_pipeworx, entity_profile), comparison, claim validation, monitoring, memory, and feedback. There are no obvious gaps for the declared domain, and a feedback tool is provided for missing functionality.

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