Stellarhound — stargazing forecasts, dark-sky sites & aurora

find_dark_sky_sites

Read-only

Recommend dark-sky sites near a location, ranked by whether the drive is worth it.

Use for "where should I drive to stargaze near X". Sites are grouped by
drivability (how far), then ranked by a drive-worth score combining the
best weather window over the next 7 nights, how much darker the site is
than the user's location, and drive time. Drive times are approximate
(straight-line x 1.3), not routed.

Args:
    lat: Latitude in decimal degrees (-90..90).
    lon: Longitude in decimal degrees (-180..180).
    limit: Max sites to return, 1-12 (default 5).

Returns {ranking (methodology note), tiers:[{tier, label, hint, sites:[...]}],
nearest_km, uncertainty_note, attribution} — sites grouped into drivability
tiers. Include the methodology note when presenting — it explains the
ordering honestly.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`lat`	Yes
`lon`	Yes
`limit`	No

TDQS

A5/5.0

Behavior5/5

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

Discloses the ranking logic (grouped by drivability, scored by weather window, darkness, drive time), notes drive times are approximate, and instructs to include the methodology note when presenting results. Adds transparency beyond the read-only/non-destructive 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?

Well-structured with clear sections: purpose, usage, ranking methodology, args. Concise yet comprehensive, no redundant content.

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?

Comprehensive for its complexity: covers purpose, usage, ranking, output structure (tiers, nearest_km, uncertainty_note, attribution), and instructs to include methodology note. No gaps in understanding how to use and interpret results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The 'Args' section fully explains lat, lon, and limit, including ranges and default, which the input schema lacks. Adds meaningful parameter 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?

Clearly states the tool recommends dark-sky sites near a location, ranked by drive worth. Differentiates from siblings by focusing on recommending specific sites rather than providing forecasts or conditions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides an explicit usage example ('where should I drive to stargaze near X') and explains the ranking methodology, making it clear when to use this tool over alternatives.

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

A4.3/5.0

Disambiguation4/5

The tools are mostly distinct: find_dark_sky_sites recommends locations, get_aurora_forecast handles aurora, get_light_pollution measures darkness, get_stargazing_conditions scores nights, and get_tonight_agenda gives a nightly plan. There is slight overlap between get_light_pollution and the light-pollution info embedded in get_stargazing_conditions and get_tonight_agenda, but the primary purposes are clear.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern: find_dark_sky_sites, get_aurora_forecast, get_light_pollution, get_stargazing_conditions, get_tonight_agenda. The verbs (find, get) and nouns are descriptive and uniform, making the naming highly predictable.

Tool Count5/5

With 5 tools, the server is well-scoped for its purpose of stargazing forecasts and dark-sky site recommendations. Each tool covers a distinct aspect of the domain without redundancy, and the count is within the ideal range for a focused MCP server.

Completeness4/5

The tool surface covers the core stargazing workflow: finding dark sites, checking aurora, assessing light pollution, scoring nights, and getting a nightly agenda. A minor gap is the lack of a tool for detailed weather forecasts beyond the stargazing score, but the existing tools handle the primary use cases effectively.

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