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Repology

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
  2. Removed
  3. Added

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, non-destructive. The description adds substantial behavioral context: history bounded by 60-day TTL, snapshots written on cache-miss, decay computed from daily closes not intraday, and interpretation of expired opportunities. This goes well beyond the structured fields.

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 dense but well-organized with sections for RESPONSE and LIMITS. Every sentence provides functional value—explaining output structure, data gaps, and interpretation nuances. No filler or redundancy.

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 tool's complexity and absence of an output schema, the description fully compensates by detailing the response shape (tracked, expired, snapshot_dates), semantics (trend, decay), and limitations (TTL, cache-miss gaps). It is complete for an agent to understand what the tool returns and how to interpret it.

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 coverage is 100%, with both parameters described. The description repeats defaults and adds minor context like 'snapshot family' but does not fundamentally enhance understanding beyond what the input schema already provides. Baseline 3 is appropriate.

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 explicitly states the tool's purpose: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots. Answers how long has this edge existed and is it shrinking?' This clearly distinguishes it from sibling tools like polymarket_edges (which likely provides current edges) by focusing on historical persistence and decay.

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 clear usage context: '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).' It implies when to use this tool (to assess edge longevity) and contrasts with alternatives via the reference to polymarket_edges, but does not explicitly name an alternative or state '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

B3.3/5.0
Disambiguation3/5

There is notable overlap among the ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and among the prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, polymarket_kalshi_spread). While descriptions differentiate them, agents may struggle to choose the appropriate one without careful reading.

Naming Consistency3/5

Most tools follow a snake_case verb_noun pattern (e.g., ask_pipeworx, compare_entities), but the Repology-specific tools break this pattern with simple nouns like maintainer, problems, project, and repositories. This inconsistency makes the overall naming feel mixed.

Tool Count2/5

With 36 tools, the set is too large for a server focused on Repology package queries. Many tools are from the Pipeworx platform and include redundant variants (e.g., ask_pipeworx_beta, polymarket_edge_tracker), inflating the count without adding substantial new functionality.

Completeness2/5

For a server named Repology, the tool surface is severely incomplete: it lacks fundamental Repology operations like detailed package comparisons, version history exploration, and repository-specific queries. Even as a general data platform, there are gaps such as no batch export or aggregate statistics.

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