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Dbnomics

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

A4.5/5.0
Behavior5/5

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

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds extensive behavioral context: it explains the response structure (tracked, expired, snapshot_dates), the meaning of trend and decay_pp_per_day, data freshness (snapshot written on cache-miss), and TTL constraints (60-day snapshot TTL).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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

The description is well-structured, front-loading the core purpose, then breaking down the response into labeled sections (RESPONSE, LIMITS). Every sentence serves a purpose, and the length is appropriate for the tool's complexity.

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 the absence of an output schema, the description thoroughly explains the return values (tracked, expired, snapshot_dates) including field meanings and limitations (e.g., decay from daily closes, not intraday). No critical information is missing.

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?

The input schema has 100% coverage with clear descriptions for both parameters (days and window). The description repeats the defaults and adds minor context (clamp range for days), but does not provide significant additional meaning beyond the 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's purpose: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots.' It answers a specific question about edge longevity and distinguishes itself from sibling tools like polymarket_edges and polymarket_arbitrage by focusing on historical persistence.

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 provides context on when to use the tool (e.g., comparing fresh vs. old edges) and includes limits on data depth and snapshot gaps. However, it does not explicitly state when not to use it or name alternatives among siblings.

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
Disambiguation2/5

Several tool families overlap heavily: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, and deep_research all route to the same underlying catalog, while bet_research, polymarket_edges, and polymarket_arbitrage all surface prediction-market opportunities. The detailed descriptions mitigate some confusion, but an agent must read carefully to avoid selecting the wrong member of these overlapping groups.

Naming Consistency3/5

The set is consistently snake_case and many tools follow verb_noun conventions like list_datasets, get_series, find_series, and validate_claim. However, a large minority are noun-led names such as entity_profile, deep_research, bet_research, pipeworx_feedback, and polymarket_arbitrage, so there is no single predictable naming pattern across the whole server.

Tool Count2/5

At 36 tools, this is well above the 25+ threshold for an over-heavy surface, and the set spans DBnomics data, prediction markets, memory, subscriptions, npm auditing, llms.txt generation, and AI-visibility checks. Several near-duplicate meta-tools could be consolidated, and unrelated domains would be better split into separate servers.

Completeness4/5

Core workflows are well covered: DBnomics browse/fetch/search, company resolve/profile/compare/change, prediction-market discovery and fill-risk, and memory/subscription lifecycles are all represented. Minor gaps exist, such as no subscription-update operation and no direct single-dataset detail fetch without listing, but agents can work around them.

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