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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).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

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 detail beyond the annotations: explains snapshot timestamps are written on cache-miss, gaps indicate no scan, history is bounded by a 60-day TTL, decay is computed from daily closes net of slippage, and expired opportunities are tracked with lifespan. This goes far beyond the read-onlyHint and provides valuable operational context.

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?

Although longer than a typical description, every sentence earns its place: it is structured into purpose, parameter clarification, response schema explanation, and limitations. The information density is high, and the phrasing is front-loaded with the core question the tool answers.

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?

For a tool with no output schema, the description fully explains the response structure (tracked[], expired[], snapshot_dates[]) and including field-level details like trend, decay_pp_per_day, and lifespan_days. It also covers edge cases like missing snapshots and historical depth limits, making it complete for the tool's complexity.

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 both parameters have descriptions, so the baseline is 3. The tool description adds value by clarifying that 'days' is a lookback and 'window' refers to the snapshot family (24hr/1wk/1mo), reinforcing the semantic meaning and tying them to the polymarket_edges concept.

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's purpose: tracking edge persistence and decay from daily polymarket_edges snapshots. It answers a specific trading question about how long an edge has existed and whether it is shrinking, which distinguishes it from sibling tools like polymarket_edges or polymarket_arbitrage that focus on current opportunities.

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 strong context on when to use the tool by framing it around the question of edge persistence versus freshness, and it explains the significance of edge age for trading decisions. It does not explicitly name alternative tools or say when not to use it, but the implied use case is clear enough for a 4.

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
Disambiguation1/5

The tool set is dominated by tools unrelated to USGS earthquakes (e.g., Polymarket betting, company profiles, memory operations). An agent would find it nearly impossible to distinguish the few earthquake-specific tools from the multitude of unrelated ones, leading to severe misselection.

Naming Consistency3/5

Most tool names follow a verb_noun pattern with underscores (e.g., search_earthquakes, count_earthquakes), which is consistent. However, the variety of verbs and domains creates a sense of incoherence, and some tool names are overly generic (e.g., process, run) in the broader context, though those are not present here. The naming pattern is acceptable but the inconsistency in domain scope reduces clarity.

Tool Count1/5

With 29 tools but only 3 directly related to earthquakes, the tool count is grossly inappropriate. The server's name suggests a focused purpose, but the vast majority of tools belong to other domains (e.g., Pipeworx queries, Polymarket betting, company data). This extreme mismatch makes the tool set bloated and misleading.

Completeness2/5

For earthquake data, the server provides only search, count, and get by ID. Missing are common operations like listing recent quakes, subscribing to alerts, or updating/correcting data. The coverage is minimal and insufficient for a comprehensive earthquake tool server, leaving significant gaps that agents could not work around.