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Acled

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

A4.6/5.0
Behavior5/5

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

The description discloses return structure (tracked[], expired[], snapshot_dates[]), computational details (trend, decay computed on |edge_pp_net|), and limitations (60-day TTL, cache-miss gaps). Annotations already indicate safe read-only, idempotent behavior; description adds rich behavioral context without 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 detailed but well-structured, front-loading purpose. Every sentence adds value, though slightly verbose with RESPONSE details. It earns its length.

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 no output schema, the description fully explains the return fields, data sources, and limitations (TTL, gaps, decay computation). It is complete for an agent to understand what to expect.

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%, so baseline is 3. The description adds meaning by explaining days default/clamp, window snapshot family, and semantics of 'lookback'. This goes beyond the schema's brief field descriptions.

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, answering specific questions about edge duration and trend. It distinguishes itself from sibling polymarket_edges by focusing on time-series and historical context.

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 context by contrasting fresh vs. old edges and explicitly framing the question 'how long has this edge existed and is it shrinking?'. However, it does not provide explicit when-not-to-use or list alternatives.

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

A3.7/5.0
Disambiguation3/5

Many tools have overlapping purposes, especially the Pipeworx query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) which all serve similar data retrieval needs. Additionally, entity_profile, compare_entities, and recent_changes overlap in providing company information. Polymarket tools also have overlapping analysis functions. This can cause confusion for agents.

Naming Consistency2/5

Tool names are inconsistent in style and convention. Some use underscores (ai_visibility_check, ask_pipeworx), others are single words (forget, recall), and many lack a clear verb_noun pattern (pipeworx_feedback, polymarket_edges). This mixture of naming conventions reduces predictability.

Tool Count3/5

With 32 tools, the count is on the higher side but appropriate given the broad scope covering multiple domains (Pipeworx data, Polymarket betting, ACLED events, npm scanning, memory, etc.). However, some areas have only one or two tools, which feels sparse, and the overall set could be trimmed or better organized.

Completeness3/5

The tool set covers many domains but has notable gaps. For ACLED, only search and count tools exist without any update/delete capabilities. For Pipeworx, the tools are heavily read-focused with no apparent write operations. The broad scope makes completeness hard to assess, but some obvious lifecycle operations are missing.

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