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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
  2. Removed
  3. Added

TDQS

A4.4/5.0
Behavior5/5

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

The description adds substantial behavioral context beyond the annotations: 60-day snapshot TTL, reliance on daily closes (not intraday), gap interpretation (cache-miss), and the absolute-value computation for decay. These details expose data limitations and calculation semantics, providing transparency the annotations don't cover.

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 longer than typical but every sentence adds value: purpose, mental model, argument context, response format, and limitations. It's front-loaded with the core purpose and structured with flags, though slightly verbose in the response section.

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 only 2 parameters and no output schema, the description fully compensates by detailing the response structure (tracked[], expired[], snapshot_dates[]), calculation methodology, and historical limitations. It provides all necessary context for an agent to use the tool effectively.

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 100% coverage for both parameters, including defaults and ranges. The description repeats this information and adds minimal context (e.g., 'lookback', 'snapshot family'), satisfying the baseline but not adding significant new meaning.

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's purpose: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots.' It answers a specific question ('how long has this edge existed and is it shrinking?') and differentiates from sibling tools like polymarket_edges by focusing on historical persistence/decay 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 Guidelines4/5

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

The description implies when to use the tool (for historical edge persistence/decay analysis) and contrasts fresh vs. old edges, but it doesn't explicitly name alternatives like polymarket_edges or state when not to use it. The guidance is clear but not fully explicit with exclusions.

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

B3.3/5.0
Disambiguation2/5

The five Zoho CRM tools are well-differentiated, but the 31 Pipeworx/Polymarket tools create heavy overlap (e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research all serve similar lookup purposes). Mixing two unrelated domains makes it easy for an agent to select a tool from the wrong group.

Naming Consistency2/5

The Zoho tools share a consistent zoho_ prefix, but the remaining 31 tools follow no clear convention—ask_pipeworx, bet_research, deep_research, entity_profile, forget, scan_dependency, etc. mix noun-first, verb-first, and bare verb patterns without a unifying scheme.

Tool Count2/5

36 tools is well beyond what a Zoho CRM server needs, and only 5 actually relate to Zoho CRM. The bulk are for unrelated data sources, prediction markets, memory management, and web generation, making the set feel bloated and unfocused.

Completeness2/5

The Zoho CRM surface includes create, get, list, and search, but omits essential operations like update, delete, and upsert. The other 31 tools cover a completely different domain, so the server fails to provide complete lifecycle coverage for its stated purpose.