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Glama

N2yo

by io.github.pipeworx-io

Whats Above

whats_above
Read-onlyIdempotent

List what satellites are currently above a location on Earth, within a given radius of the observer's zenith. Use this to answer "what satellites are overhead right now?" Optionally filter by category (e.g. 52 = Starlink, 18 = amateur radio, 1 = brightest). Example: whats_above({ lat: 40.71, lon: -74.0 }).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latYesObserver latitude in decimal degrees.
lonYesObserver longitude in decimal degrees.
radiusNoSearch radius in degrees from the observer zenith (default 70, max 90).
_apiKeyNoOptional — your own N2YO API key for higher limits; omit to use the shared Pipeworx key.
altitudeNoObserver altitude in meters above sea level (default 0).
categoryNoN2YO category filter: 0 = all (default), 18 = amateur radio, 52 = Starlink, 1 = brightest.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "lat": 40.71,
+    "lon": -74
+  },
+  {
+    "category": 52,
+    "lat": 51.5,
+    "lon": 0,
+    "radius": 45
+  }
+]
  2. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, so the safety profile is covered. The description adds the radius and category filter behavior but does not disclose potential quirks like API rate limits or response size. It does not contradict annotations, so a mid-range score is fair.

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 two concise sentences plus a short example call. It front-loads the core action, includes the intended use, and provides a concrete invocation. Every part earns its place with no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only list tool with 6 well-documented parameters and no output schema, the description gives enough context to invoke correctly: purpose, scope, optional filtering, and an example. It doesn't explain the return format in detail, but 'List what satellites' implies a list of satellite objects, which is reasonable without an output schema. Minor gap but overall sufficient.

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?

Schema description coverage is 100%, so all parameters are documented structurally. The description adds examples (e.g., category 52 = Starlink) and an example call, which is helpful but not essential given the schema. It does not introduce meaning beyond the schema, so baseline 3 applies.

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 verb ('List') and a clear resource ('satellites currently above a location on Earth'), and further scopes it by 'within a given radius of the observer's zenith.' This clearly distinguishes it from siblings like get_positions or get_visual_passes. The example call reinforces the 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?

It explicitly states the intended use case ('Use this to answer "what satellites are overhead right now?"') and shows a concrete example. It does not explicitly list when not to use it or name alternatives, but for a simple query tool, the context is clear enough. A 4 is appropriate for clear context without exclusions.

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

A3.8/5.0
Disambiguation2/5

Multiple tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicates (beta is currently identical), and the five Polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) require careful reading to distinguish. Additionally, entity_profile, compare_entities, and recent_changes all handle company data, and ai_visibility_check vs scan_competitor_ai_presence are clearly paired. The detailed descriptions help, but an agent will frequently misselect among these clusters.

Naming Consistency3/5

All names are lowercase with underscores, so the style is internally consistent. However, the pattern is mixed: many use verb_noun (get_positions, list_subscriptions, resolve_entity) but several are noun-first or noun-only (entity_profile, polymarket_edges, pipeworx_trending, bet_research). There's no strong verb/noun convention across the set, making the naming pattern less predictable than it could be.

Tool Count2/5

With 34 tools, the set exceeds the 16–25 'heavy' range and sits in the 'too many' band. The server name suggests a focused satellite-tracking service, yet only 3 tools (get_positions, get_visual_passes, whats_above) serve that purpose; the other 31 cover unrelated domains like data research, prediction markets, memory, and subscriptions. Even as a general-purpose research platform, the count feels bloated and unfocused.

Completeness3/5

The tool surface is broad, covering satellite tracking, data research, prediction markets, subscriptions, and memory, and within each cluster the main operations exist (e.g., subscription lifecycle, edge analysis + fill risk). However, the scattered scope creates gaps: there's no direct raw-data fetch tool (everything goes through ask_pipeworx), no general web search, and the presence of unrelated utilities (generate_llms_txt, scan_dependency) suggests the domain boundaries are unclear. For its stated satellite purpose, the satellite tools are thin (no TLE, no catalog, no detailed orbit info).

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