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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

A4.7/5.0
Behavior5/5

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

The description adds substantial behavioral context beyond the readOnlyHint and destructiveHint annotations. It discloses that history depth is bounded by a 60-day TTL, that snapshot gaps occur due to cache-misses, and that decay numbers come from daily closes net of default slippage. These are non-obvious traits that help the agent anticipate limitations and interpret results.

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 long but every sentence carries essential information. It front-loads the core purpose, then logically covers arguments, response structure, and limitations. There is no fluff or repetition; the density is justified by the tool's complexity and the absence of an output schema.

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 lack of an output schema, the description is remarkably complete. It explains the full response anatomy: tracked[] with time-series fields, expired[] with lifespan comparisons, and snapshot_dates[] with gap semantics. It also covers edge cases like TTL limits and non-intraday data, leaving little ambiguity for the agent.

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%, and the schema already describes defaults and clamping for both parameters. The description adds semantic meaning by explaining 'window' as a 'snapshot family' with the default '1wk', and clarifies that 'days' controls the lookback depth. This goes slightly beyond the schema, justifying a score above the baseline 3.

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 a specific purpose: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots.' It uses a strong verb and resource, and directly distinguishes itself by answering 'how long has this edge existed and is it shrinking?' This differentiates it from sibling tools like polymarket_edges (current snapshots) and polymarket_arbitrage.

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 provides clear context for when to use the tool: when assessing whether an edge is fresh or old, noting that 'a fresh wide edge and a 3-week-old wide edge are different trades.' It effectively implies the tool is for historical persistence analysis. However, it does not explicitly name alternative tools or state when not to use it, so it falls short of a 5.

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

A3.9/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same 5,529-tool catalog, with ask_pipeworx_beta currently identical to ask_pipeworx. The Polymarket suite also has ambiguous boundaries (bet_research vs. polymarket_edges vs. polymarket_arbitrage), and scan_competitor_ai_presence simply wraps ai_visibility_check, so agents may struggle to choose the right tool.

Naming Consistency3/5

All names are snake_case and readable, but the set mixes verb-led names (ask_pipeworx, compare_entities, scan_dependency, validate_claim, hts_search) with noun-led names (entity_profile, recent_alerts, polymarket_edges, ai_visibility_check). Variant suffixes like ask_pipeworx_beta / ask_pipeworx_grounded add further inconsistency, so the naming is coherent enough but not predictable enough for a 4.

Tool Count2/5

At 33 tools, the server exceeds the 25-tool threshold for 'too many' in the rubric. Even though the broad data-research scope justifies a larger surface, the count feels bloated because several tools are near-duplicates (e.g., ask_pipeworx_beta, ask_pipeworx_grounded) or wrappers (scan_competitor_ai_presence), making the set heavier than necessary.

Completeness4/5

The server covers its core domains thoroughly: data querying (ask_pipeworx family, deep_research), entity resolution and comparison (resolve_entity, entity_profile, compare_entities), verification (validate_claim, ask_pipeworx_grounded), HTS tariff lookup (search + detail), subscription lifecycle (subscribe/unsubscribe/list/recent_alerts), and memory (remember/recall/forget). Minor gaps exist, such as no subscription-update endpoint and no generic raw-source export, but these are workaround-able and do not cause agent failures.

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