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videogames

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. Added
  2. Removed
  3. Added

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare read-only and idempotent, but the description goes far beyond that. It discloses data freshness behavior (snapshots written on cache-miss causing gaps), the 60-day TTL bound, the fact that decay is computed from daily closes rather than intraday, and the exact semantics of response fields like first_seen, trend, and decay_pp_per_day. This is rich behavioral context that helps the agent set expectations.

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 is substantive. It is cleanly structured with 'Args:', 'RESPONSE:', and 'LIMITS:' sections, making it easy to scan. The opening sentence immediately conveys the core value proposition. No filler or redundant restatement of the tool name or annotations.

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 carries the full burden of explaining return values, and it does so exhaustively: tracked[], expired[], and snapshot_dates[] are all defined with their inner fields and semantics. It also covers edge cases (gaps, age limits, signed edge values). This is a model of completeness for a telemetry tool.

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?

The schema covers both parameters completely (100% coverage), so the baseline is 3. The description adds meaning beyond the schema by defining 'window' as a 'snapshot family' and explaining that snapshot dates correspond to days with actual data. It also reiterates defaults and clamps in a way that reinforces the schema. This extra context nudges the score to 4.

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 a specific, resource-bound statement: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots.' It clearly answers a distinct question ('how long has this edge existed and is it shrinking?') and differentiates from sibling tools like polymarket_edges by focusing on persistence and decay rather than edge computation. It also explicitly frames the output (tracked/expired) which further clarifies purpose.

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 usage context by stating the exact question it answers and contrasting fresh vs. old wide edges. It also explains the data source (daily snapshots) and limitations (TTL, cache-miss gaps). However, it does not explicitly name sibling tools to use instead, nor does it provide when-not-to-use guidance, so it stops 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

A3.6/5.0
Disambiguation2/5

Heavy overlap within the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta which is currently identical, ask_pipeworx_grounded, deep_research) and among prediction market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) makes selection genuinely ambiguous. Some tools are distinct (remember/recall/forget), but the clustering blurs boundaries.

Naming Consistency2/5

Conventions are mixed: some tools use verb_noun (list_games, get_game, filter_games, subscribe, unsubscribe, recall, remember) while others use ad-hoc noun phrases or brand-style names (polymarket_arbitrage, entity_profile, deep_research, ask_pipeworx, bet_research). No single pattern dominates, making it harder to predict tool names.

Tool Count1/5

34 tools is heavy, and the mismatch is severe: the server is named 'videogames' but only 3 of 34 tools (list_games, get_game, filter_games) have any relation to video games. The bulk are Pipeworx data/prediction-market/memory utilities that do not belong under this server's apparent purpose. This is a fundamental scope failure.

Completeness2/5

As a video game server, the surface is extremely thin: only list/get/filter by tags and platform, with no search by name, no CRUD, no reviews, no categories beyond the fixed filter set. The other 31 tools are irrelevant to the videogames domain, so the stated purpose is largely uncovered. The mismatch makes coverage assessment nearly impossible for the actual server name.

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