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Data Gov Sg

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

A4.7/5.0
Behavior5/5

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

Annotations already indicate readOnly, idempotent, not destructive. The description adds rich details: snapshots written daily on cache-miss, 60-day TTL, decay from daily closes (not intraday), and explains the response structure extensively.

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: starts with purpose, then args, then detailed response fields, then limits. Every sentence adds value; no extraneous content.

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?

With no output schema, the description fully explains the return structure (tracked, expired, snapshot_dates) and their meanings. It also covers limitations (TTL, snapshot start) and computation details.

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% with descriptions. The description adds defaults (14, 1wk), clarifies 'window' as snapshot family, and explains the response includes tracked, expired, and snapshot_dates arrays. This adds value 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' and answers the specific question 'how long has this edge existed and is it shrinking?' It distinguishes from siblings like polymarket_edges by focusing on historical tracking rather than current snapshots.

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?

Provides context with the example of a fresh vs. 3-week-old wide edge, implying when to use this tool for temporal analysis. However, it does not explicitly contrast with sibling tools or state when not to use it.

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 tools have heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and the Polymarket and company-research toolsets overlap significantly (bet_research vs polymarket_edges, entity_profile vs compare_entities vs recent_changes). Even with strong descriptions, an agent can easily misselect among these near-duplicate entry points.

Naming Consistency3/5

Names are readable but mix conventions: verb_noun forms (search_datasets, query_dataset, generate_llms_txt, validate_claim) coexist with noun/adjective forms (air_quality_pm25, taxi_availability, entity_profile, polymarket_edges). There is no single predictable pattern, though the domain-prefix style for Singapore data tools is consistent.

Tool Count2/5

40 tools is far too many for a server nominally scoped to Singapore government data. The bulk of the surface is a general-purpose Pipeworx/prediction-market/research toolkit that has nothing to do with Data Gov Sg, so the actual Singapore dataset tools are buried under dozens of unrelated capabilities.

Completeness4/5

For the core data.gov.sg use case, the surface is solid: search_datasets, get_dataset, and query_dataset cover dataset discovery and retrieval, supplemented by live-data tools (weather_now, air_quality_psi, traffic_incidents, taxi_availability, uv_index). The broader Pipeworx side also includes helpful auxiliary lifecycle tools like discover, subscribe, recent_alerts, memory, and feedback, so there are no critical dead ends.

Resources