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flights

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?

The description goes well beyond the annotations (readOnlyHint, idempotentHint) by detailing response structure, the meaning of expired opportunities, snapshot date gaps, the 60-day TTL limitation, and the computation of decay numbers from daily closes. This rich context is highly valuable and does not contradict any 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 well-structured with Args, RESPONSE, and LIMITS sections. Every sentence contributes meaningful information, and the purpose is front-loaded. Despite its length, it is densely packed and 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 explains the return values (tracked[], expired[], snapshot_dates[]) including field details. It also covers limitations, prerequisites, and edge cases (snapshot gaps, TTL), making the tool's behavior and expectations clear for an agent.

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%, so baseline is 3. The description adds value by explaining the 'snapshot family' concept for the window parameter and how days affect the response (e.g., snapshot_dates shows actual data availability). It reinforces defaults and clamps, which helps but is somewhat redundant with schema.

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 specific purpose: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots' and answers a concrete question ('how long has this edge existed and is it shrinking?'). It distinguishes itself from sibling tools like polymarket_edges by focusing on the time dimension and decay analysis, not just current edge values.

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 gives strong contextual guidance: it explains that a fresh edge and a 3-week-old edge are different trades, implying when historical persistence matters. It does not explicitly name alternatives or state when-not to use the tool, but the context is clear enough for an agent to infer appropriate use cases.

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

Several tools have heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical, ask_pipeworx_grounded and deep_research blur the query/research boundary, and the polymarket_* family plus bet_research all target prediction-market analysis. An agent would frequently struggle to pick the correct tool among these near-duplicates.

Naming Consistency3/5

All names are snake_case, but the verb/noun style is inconsistent: get_* for flight lookups, ask_* for queries, noun-style names like entity_profile and bet_research, and the polymarket_* prefix group. Some subgroups are internally consistent, but there is no single predictable pattern across the set.

Tool Count2/5

36 tools is far too many for a server named 'flights' — only 5 tools actually relate to aviation, while the rest span prediction markets, SEC/FDA data, npm packages, memory, and feedback. Even viewed as a general data platform, the count is heavy and the scope is unfocused.

Completeness2/5

For a flights server, the surface is severely incomplete: no scheduled flight status, delays, cancellations, or airport schedules — only live ADS-B snapshots. For the broader data-research domain the tools imply, coverage is better but still scattered, with no coherent lifecycle and several one-off utilities that don't connect to the rest.

Resources