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Fda Devices

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

A4.3/5.0
Behavior5/5

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

The description goes far beyond annotations, explaining response structure (tracked, expired, snapshot_dates), limitations (60-day TTL, cache-miss gaps), and computational details (decay from daily closes, signed edge_pp_net). No annotation contradictions.

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 relatively long but well-structured: purpose statement, args, response fields, and limits. Each sentence adds value. However, it could be slightly more concise by reducing redundancy in the response field explanations.

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?

Despite no output schema, the description thoroughly explains all return fields and their semantics. It also covers limitations and operational details (cache-miss gaps, TTL). This gives the agent a complete understanding of what to expect.

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 both parameters are documented. The description adds context: default values (14 days, '1wk'), max for days (30), and explanation of 'window' as snapshot family. This adds meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool provides 'edge persistence and decay telemetry' and answers a specific question about edge duration and decay. The verb is implied (track/analyze), and the resource (polymarket edges over time) is clear. However, it does not explicitly differentiate from sibling tools like polymarket_edges, which likely focus on current edges without time-series.

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 a direct usage context: 'Answers how long has this edge existed and is it shrinking?' This tells the agent when to use this tool. It does not explicitly exclude other tools or state when not to use it, but the context is sufficient. No explicit alternatives are mentioned.

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

A3.5/5.0
Disambiguation2/5

The FDA-specific tools are distinct, but they are mixed with many generic Pipeworx tools (e.g., ask_pipeworx variants, deep_research, entity_profile) that have overlapping purposes. This creates significant ambiguity for an agent trying to choose the right tool for FDA-related queries.

Naming Consistency2/5

Tool names follow two inconsistent patterns: FDA tools use 'fda_device_*' (consistent), while generic tools use various patterns like 'ask_pipeworx', 'deep_research', 'remember', etc. The mix of snake_case, camelCase, and descriptive phrases lacks coherence.

Tool Count2/5

With 37 tools, the count is high for what is intended as an FDA devices server. Only 6 tools are directly FDA-related; the rest are generic and dilute the purpose. The scope is mismatched, making the tool count inappropriate.

Completeness3/5

The FDA tools cover key areas: 510k search, adverse events, PMA, recalls, company profiles. However, the server is incomplete for its name because it lacks many tools that a comprehensive FDA devices server would have, and the generic tools don't fill those gaps.

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