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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).

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds rich behavioral context: 60-day snapshot TTL, cache-miss snapshot writing, daily-close decay calculation (not intraday), and the distinction between tracked and expired opportunities. It also clarifies that negative edge_pp_net means SELL YES. No contradiction with 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 lengthy but well-structured with labeled sections (Args, RESPONSE, LIMITS) and front-loaded with the key value proposition. Every sentence conveys useful information, but it is more verbose than necessary; some minor trimming could improve conciseness without losing meaning.

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 thoroughly explains the response structure: tracked[] (with time-series, first_seen, trend, decay_pp_per_day), expired[] (with lifespan_days), and snapshot_dates[] (with gap semantics). It also documents limitations (TTL, snapshot gaps) and data provenance. This is complete for an agent to understand behavior and invoke correctly.

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 provides full coverage of both parameters (days with default/clamp, window with allowed values and default). The description's mention of 'days (lookback, default 14, max 30)' and 'window (snapshot family, default "1wk")' essentially repeats the schema. It adds no new syntax or parameter-level semantics beyond what the schema already states.

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 identifies the tool as 'edge persistence and decay telemetry built from daily polymarket_edges snapshots' and answers the core question 'how long has this edge existed and is it shrinking?'. It distinguishes itself from sibling tools like polymarket_edges by focusing on time-series analysis rather than current edge values.

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 ('a fresh wide edge and a 3-week-old wide edge are different trades'), implying use for edge-age analysis. It explains the relationship to polymarket_edges snapshots but does not explicitly name alternative tools or state exclusions, so it stops short of full when/when-not 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

A3.6/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded differ only in mode, and the polymarket_* cluster (polymarket_edges, polymarket_edge_tracker, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread) all target prediction-market analysis with fuzzy boundaries. The presence of ai_visibility_check and scan_competitor_ai_presence, plus discover_tools and suggest_questions, adds further ambiguity about which tool to select first.

Naming Consistency3/5

Names are all snake_case but follow mixed conventions: verb_noun (list_feeds, read_feed, fetch_feed, validate_claim) coexists with noun_phrase (entity_profile, recent_changes, polymarket_edges) and prefix-grouped names (ask_pipeworx*, polymarket_*). While subgroups are internally consistent, the overall set lacks a unified pattern, though it remains readable.

Tool Count2/5

34 tools is well above the 25+ threshold for too many, and the count is especially inappropriate for a server named 'Sports Feeds' — most tools are generic data-research or meta-tools (subscriptions, memory, feedback, discovery) unrelated to sports feeds. The bloat suggests the server is actually a broad Pipeworx gateway, not a focused sports feeder.

Completeness2/5

For a sports-feeds server, only list_feeds, read_feed, and fetch_feed address the core domain, and there is no feed search, categorization beyond a simple list, or sports-specific analytics. While the general research surface (SEC, FDA, economics, prediction markets) is fairly comprehensive, it is misaligned with the stated server purpose, leaving the actual sports-feed functionality thin and with obvious gaps.