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Stat Ee

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

A4.4/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so those basics are covered. The description goes far beyond by disclosing snapshot-gap behavior ('snapshots are written when polymarket_edges runs on a cache-miss'), the 60-day TTL limit, that decay is computed from daily closes not intraday, and that expired opportunities disappear because they are 'closed, resolved, or arbed away'. This rich behavioral context is genuinely useful for an agent.

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 detailed but logically structured with sections for Purpose, Args, RESPONSE, and LIMITS. Each sentence carries information, and the RESPONSE section's field-level detail is essential given the lack of an output schema. While longer than average, the complexity of the tool's output justifies the length. A slight trim of the explanatory parenthetical about trading would make it tighter, but it is not wasteful.

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 no output schema, the description fully documents the return structure: tracked[] with its fields (edge_pp_net time-series, first_seen, trend, decay_pp_per_day), expired[] with lifespan_days, and snapshot_dates[]. It also explains data limitations (TTL, snapshot gaps) and computation methodology. Given the tool's complexity and the available structured annotations, this description is complete enough for an agent to invoke and interpret responses confidently.

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?

Input schema coverage is 100%, with defaults and allowed values for both parameters (days: 'default 14, clamp 2-30'; window: '24hr | 1wk | 1mo'). The description restates these but adds the concept of 'snapshot family' for window, which adds minimal semantic insight beyond the schema. Since the schema already provides complete parameter documentation, the baseline of 3 is appropriate; no meaningful extra meaning is provided.

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 opens with 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots' and explicitly frames the tool's purpose via the question 'how long has this edge existed and is it shrinking?' It clearly differentiates from the sibling tool polymarket_edges by focusing on historical persistence and trends rather than current edge data. The verb 'Answers' combined with the resource 'edge persistence and decay telemetry' is specific and unambiguous.

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 by answering 'how long has this edge existed and is it shrinking?' and offers an interpretive insight ('a fresh wide edge and a 3-week-old wide edge are different trades'). This implies when to use the tool, but it does not explicitly name alternatives or state 'use this instead of X'. The mention of being 'built from daily polymarket_edges snapshots' hints at the relationship to sibling tools, but an explicit exclusion would elevate it to 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
Disambiguation3/5

Most tools have fairly distinct action/resource targets and the descriptions carefully separate entry points like ask_pipeworx, deep_research, and ask_pipeworx_grounded. However, ask_pipeworx_beta is explicitly an identical clone of ask_pipeworx right now, and a few related pairs (ai_visibility_check vs scan_competitor_ai_presence, stat_ee_find_table fetch_latest vs estonia_average_wage) add ambiguity.

Naming Consistency3/5

Names are consistently lowercase snake_case and verb-led names like resolve_entity, query_table, and suggest_questions are clear. But the set mixes conventions: bare nouns (subjects, recall, forget), adjective-noun phrases (recent_alerts, recent_changes), no-verb names (estonia_average_wage, table_meta), and multiple prefixes (pipeworx_*, polymarket_*, stat_ee_*). It is readable but not a single predictable pattern.

Tool Count2/5

36 tools is well above the 15-tool threshold for a well-scoped server, and the set spans many unrelated domains: Estonian statistics, Pipeworx research, Polymarket betting, AI visibility, npm scanning, memory, and subscriptions. There is also clear redundancy (ask_pipeworx_beta duplicates ask_pipeworx, ai_visibility_check could be folded into scan_competitor_ai_presence). This feels scattered for a server named 'Stat Ee'.

Completeness4/5

For the broad data-research/agent-assistant purpose, key workflows are well covered: discovery/query/grounded/deep research, entity resolution/profile/compare/validate/recent changes, complete memory CRUD, subscription CRUD with alert feeds, and a full Polymarket edge/arb/fill-risk suite. The main gap is not missing operations within these workflows but rather the overall scope being too broad and unfocused.

Resources