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spacex

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).

TDQS

A4.5/5.0
Behavior5/5

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

Beyond annotations (readOnly, idempotent), the description discloses critical behavioral traits: snapshots are only written on cache-miss so gaps mean 'nobody scanned that day,' history is bounded by a 60-day TTL, and decay numbers come from daily closes rather than intraday data. This adds substantial context beyond the annotations.

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 compact and well-structured: it opens with purpose, then explains arguments, response structure, and limitations in distinct sections. Every sentence carries information density, using clear labels for fields and outputs.

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 the absence of an output schema, the description thoroughly defines the response fields (tracked, expired, snapshot_dates) with their semantics and caveats. It also covers data availability and computation methodology, making the tool fully comprehensible.

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 complete descriptions for both parameters (days, window) with 100% coverage, so the baseline is 3. The description mostly repeats schema details (default, max) and adds the phrase 'snapshot family' for window, but does not add 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 it provides 'edge persistence and decay telemetry' and answers a specific question: 'how long has this edge existed and is it shrinking?' It distinguishes itself from sibling tools like polymarket_edges by focusing on historical snapshots and evolution 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 conveys a clear use case (comparing fresh vs. old edges, understanding decay) and references polymarket_edges as the data source. It does not explicitly name alternative tools or provide exclusions, but the context strongly implies when this tool is appropriate.

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

The server is named 'spacex' but contains a vast number of tools unrelated to SpaceX, such as polymarket arbitrage, npm package scanning, and claim validation. Users and agents would struggle to determine whether this server is for SpaceX data or general-purpose queries.

Naming Consistency2/5

The SpaceX-specific tools follow a consistent 'get_' pattern, but the majority of tools use varied naming conventions (e.g., 'ask_pipeworx', 'bet_research', 'remember', 'subscribe'). No unifying pattern across the set.

Tool Count3/5

36 tools is a moderately large count, not extreme. However, many tools are unrelated to the server's namesake, making the set feel bloated and unfocused. The count could be trimmed to improve coherence.

Completeness2/5

For the SpaceX domain, the coverage is decent (launches, rockets, crew, Starlink). But the server's purpose is unclear—there are glaring gaps in a unified vision, as the Pipeworx tools are not integrated into a coherent SpaceX theme.

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