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Data Mesa

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

A4.6/5.0
Behavior5/5

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

The description provides extensive behavioral details beyond the annotations (readOnlyHint, idempotentHint). It explains the response structure (tracked, expired, snapshot_dates), limits (60-day TTL), how gaps occur, and how decay is computed. 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 front-loaded with the core purpose and then provides detailed context. It is somewhat verbose but every sentence adds useful information. The structure is logical, moving from purpose to arguments to response to limits.

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 having no output schema, the description thoroughly explains the response fields (tracked, expired, snapshot_dates) and their meanings. It covers all relevant context such as limits, gaps, and computation details, making it self-sufficient for an AI 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?

The input schema already covers both parameters with descriptions. The description adds value by specifying default values, clamping for days, and clarifying the meaning of the window parameter (24hr, 1wk, 1mo) beyond its schema 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 snapshots. It answers a specific question about edge duration and trend, and distinguishes itself from sibling tools like polymarket_edges by focusing on persistence rather than raw edge data.

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 good context on when to use the tool—checking edge age and trend—and contrasts a fresh wide edge with an old one. However, it does not explicitly state when not to use it or mention alternatives like polymarket_edges for snapshot data.

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

A3.6/5.0
Disambiguation3/5

Several tool groups have overlapping purposes (e.g., ask_pipeworx variants, polymarket research tools, visibility checks), despite detailed descriptions. An agent may struggle to choose between closely related options.

Naming Consistency2/5

Tool names mix snake_case verb_noun patterns (e.g., ai_visibility_check, compare_entities) with noun-heavy compound names (e.g., polymarket_arbitrage, deep_research) and single-word names (e.g., query, recall). No consistent convention.

Tool Count2/5

With 34 tools, the surface is heavy for a research/data server. Many tools are variants of core capabilities (ask_pipeworx, polymarket edges), suggesting consolidation would improve usability.

Completeness3/5

The server covers a broad range: data research, prediction markets, entity profiles, monitoring. However, the abundance of specialized variants and gaps in unified workflows (e.g., needing separate tools for simple vs grounded queries) reduce completeness.

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