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Data Gov In

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

A4.4/5.0
Behavior5/5

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

Annotations already declare read-only/idempotent/non-destructive, and the description adds substantial behavioral detail: data source from daily snapshots, response structure, snapshot gap semantics, and the nuance that edge_pp_net is signed by trade direction. It also clarifies that decay numbers come from daily closes, not intraday, and explains TTL bounds—all beyond what annotations provide.

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 organized into Args, RESPONSE, and LIMITS sections, front-loaded with purpose. It is dense but every sentence adds operational value, such as the interpretation of median lifespan as a competition clock and the note about snapshot gaps. Slightly long, but justified by the 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?

With no output schema, the description fully specifies the response shape: tracked[], expired[], snapshot_dates[], including field-level semantics (trend values, decay_pp_per_day, lifespan_days). It also covers edge cases like snapshot gaps and data history bounds, making it highly complete for an agent to invoke and interpret results.

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 already documents both parameters with descriptions, defaults, and clamps (100% coverage). The tool description only restates the default values ('default 14, max 30' and 'default "1wk"') without adding new semantic meaning or context, so it remains at the baseline.

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 its function: 'Edge persistence and decay telemetry' and the exact question it answers ('how long has this edge existed and is it shrinking?'). This distinguishes it from sibling tools like polymarket_edges, which presumably provides current edge data, 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 establishes a clear use case ('Answers ... is it shrinking?') and provides practical limits (60-day TTL, snapshot gaps). It does not explicitly name alternatives or when-not-to-use, but the context is sufficient for an agent to infer when to select this tool.

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

A3.8/5.0
Disambiguation2/5

Multiple tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer natural-language data questions. The six Polymarket/prediction-market tools also blur together, and ai_visibility_check versus scan_competitor_ai_presence are near-duplicates.

Naming Consistency4/5

Snake_case is used consistently, and most tools follow a verb-first or domain-prefixed pattern (ask_pipeworx, compare_entities, subscribe, polymarket_*). Minor deviations like entity_profile, resource_data, and ai_visibility_check are noun-first, but nothing is chaotic or mixed-cased.

Tool Count2/5

33 tools is excessive for a server named 'Data Gov In' whose actual domain-specific surface is only resource_data and resource_meta. The rest are generic Pipeworx, prediction-market, memory, and utility tools that do not belong to the apparent India open-data scope.

Completeness1/5

For a data.gov.in server, the surface is severely incomplete: there is no way to search or list datasets/resources, only fetch metadata and data for a known resourceId. The overwhelming majority of tools serve unrelated domains, so an agent using this server for Indian government data will hit dead ends immediately.

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