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Macro Snapshot

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

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint. Description adds valuable context: that decay is computed from daily closes (not intraday), that snapshots have a 60-day TTL, and that gaps in snapshot_dates indicate no scan occurred. This goes beyond 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 dense and informative but could be more structured. It front-loads key purpose but then includes response format and limits in a single paragraph, which may be harder to parse.

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 fully explains the response structure (tracked, expired, snapshot_dates) with field meanings. It also covers limits and assumptions, making it complete for a read-only data tool.

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 description coverage is 100%, and the description restates the parameters with default and max values, which adds minimal value. Baseline 3 is appropriate since the schema already covers semantics.

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 it provides edge persistence and decay telemetry from daily snapshots, answering how long an edge has existed and whether it's shrinking. This distinguishes it from siblings like polymarket_edges (which likely provides current edges) and polymarket_arbitrage.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly explains when to use: to distinguish between a fresh wide edge and an old one, with the example that a 3-week-old wide edge exists for a reason. Also details output structure and limits, though does not explicitly say when not to use.

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

A3.9/5.0
Disambiguation2/5

Several tools form near-overlapping clusters: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research all route questions, and bet_research/polymarket_edges/polymarket_arbitrage/polymarket_fill_risk/polymarket_kalshi_spread all target prediction-market edges. ask_pipeworx_beta is explicitly identical to ask_pipeworx today, so an agent must read long descriptions to pick correctly. Most other tools are distinct, but the ambiguous clusters pull the score down.

Naming Consistency3/5

All names use snake_case and are readable, but conventions mix: many are verb_noun (ask_pipeworx, compare_entities, discover_tools, subscribe), several are noun phrases (macro_snapshot, indicator, entity_profile, polymarket_arbitrage), and a few are adjective_noun (recent_alerts, deep_research). The near-duplicate ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded suffixes form the only consistent family, but overall the naming pattern is not uniform.

Tool Count2/5

33 tools is well beyond the 15-tool well-scoped range and even past the 25-tool 'too many' threshold. The server tries to be a data router, prediction-market desk, AI visibility checker, memory store, and subscription manager all at once, and includes an intentional duplicate (ask_pipeworx_beta). Several tools (remember/recall/forget, subscribe/unsubscribe/list_subscriptions/recent_alerts) could be their own server.

Completeness4/5

The surface covers question answering, deep research, entity resolution/profile/comparison, macro indicators, prediction-market analytics, subscriptions, memory, and feedback—no obvious dead ends for the main workflows. Minor gaps exist (e.g., no direct generic web search, no update for saved memory other than overwrite, and some niche additions like generate_llms_txt feel out of place), but the core data and research lifecycle is well covered.

Resources