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Glama

N2yo

by io.github.pipeworx-io

Get Visual Passes

get_visual_passes
Read-onlyIdempotent

Find when a satellite will be visible to the naked eye overhead from an observer location (visual passes = bright, sunlit passes against a dark sky). Returns start/max/end times, elevation, azimuths, brightness magnitude, and duration. Use this for "when can I see the ISS pass over?" Example: get_visual_passes({ norad_id: 25544, lat: 40.71, lon: -74.0 }).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latYesObserver latitude in decimal degrees.
lonYesObserver longitude in decimal degrees.
daysNoNumber of days ahead to search for passes (default 5, max 10).
_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).
norad_idYesNORAD catalog id of the satellite, e.g. 25544 for the ISS.
min_visibilityNoMinimum visible duration in seconds for a pass to be returned (default 300).

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,
+    "norad_id": 25544
+  },
+  {
+    "days": 10,
+    "lat": 40.71,
+    "lon": -74,
+    "min_visibility": 600,
+    "norad_id": 25544
+  }
+]
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds behavioral context by detailing the return data (start/max/end times, elevation, azimuths, brightness magnitude, duration) and explaining the visual pass definition, which goes beyond the annotations without contradicting them.

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 three concise sentences plus an example, front-loading the core purpose. Every sentence contributes: definition, return values, and a usage example. No redundant or filler content.

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?

With no output schema, the description appropriately enumerates the returned fields and provides a concrete example. It covers the essential context for a satellite pass query, though it does not address edge cases like no passes found or rate-limit behavior, but those are partially handled by annotations and schema.

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% with descriptive parameter definitions. The description complements this by providing a real-world example (ISS with lat/lon) and clarifying the meaning of 'visual passes' in relation to parameters like min_visibility. This adds value beyond the schema alone.

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 uses a specific verb ('Find') and resource ('satellite visual passes'), clearly defining the scope as naked-eye visibility for an observer location. It distinguishes from sibling tools like get_positions by emphasizing 'bright, sunlit passes against a dark sky' and includes a concrete use case ('when can I see the ISS pass over?').

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?

Provides a clear use case ('when can I see the ISS pass over?') and a concrete example call, establishing context for when to use the tool. However, it does not explicitly mention alternatives or when not to use it, such as differentiating from get_positions or whats_above.

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).

Resources