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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. First observed

TDQS

A4.2/5.0
Behavior5/5

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

Annotations confirm safe, read-only, idempotent behavior. The description adds extensive behavioral details: data from daily snapshots, explanation of response structure (tracked, expired, snapshot_dates), limits (60-day TTL, decay from daily closes), and handling of gaps. This goes well beyond annotations.

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 information-dense and front-loaded with purpose. While it is somewhat long, every sentence adds value, and the structure follows a logical flow from purpose to response format to limitations. Conciseness is good given 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?

No output schema exists, so the description fully explains return values (tracked, expired, snapshot_dates) with their fields and meanings. It also covers data limits and sources, making the tool's behavior completely understandable for a read-only telemetry tool.

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 clear descriptions. The description reiterates parameter defaults and bounds but does not add significant new meaning beyond what the schema already provides. Baseline of 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 clearly states the tool provides edge persistence and decay telemetry from daily snapshots, answering a specific question about edge duration. It distinguishes itself from sibling polymarket_edges by focusing on time-series analysis rather than current edges.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for analyzing edge history but does not explicitly state when not to use or suggest alternatives like polymarket_edges for current snapshots. It offers implicit context but lacks explicit guidance.

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

A4/5.0
Disambiguation4/5

Tool purposes are mostly distinct, with detailed descriptions differentiating similar-sounding tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research. However, some overlap exists between bet_research and polymarket_* tools, requiring careful reading of descriptions to select the correct one.

Naming Consistency3/5

Tool names mix verb_noun patterns (e.g., resolve_entity, validate_claim) with noun_phrases (e.g., entity_profile, recent_alerts) and occasional inconsistencies like pipeworx_trending or search_within. While most names are readable, the lack of a uniform convention makes it harder to predict tool names.

Tool Count4/5

32 tools cover a broad domain of data retrieval, analysis, prediction markets, and system management. The count is high but reasonable given the extensive feature set, though a more focused set could improve coherence.

Completeness4/5

The tool set covers major needs: data lookup, comparison, validation, monitoring, and memory. Missing CRUD operations for external data are expected as this is a read-centric API, so gaps are minor and don't hinder common tasks.

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