Stellarhound — stargazing forecasts, dark-sky sites & aurora

get_light_pollution

Read-only

Estimate light pollution at any point: approximate Bortle class and what's visible there.

Use for "how dark is the sky at X" / "can I see the Milky Way from X".
Returns the zenith-brightness-derived estimate (mpsas, approximate Bortle
class) and a plain-language ceiling: what the Milky Way, naked-eye and
telescope views are like under that sky. This is a satellite-data estimate,
not a field measurement — say so.

Args:
    lat: Latitude in decimal degrees (-90..90).
    lon: Longitude in decimal degrees (-180..180).

Returns {light_pollution:{bortle_approx, mpsas, ceiling:{label, milky_way,
naked_eye, telescope}, note}, attribution}.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`lat`	Yes
`lon`	Yes

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the description doesn't repeat safety info. It adds value by clarifying the estimate is satellite-based and the ceiling is a plain-language summary, which is behavioral context beyond annotations. No 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 well-structured with clear sections, a usage example, and parameter list. A bit verbose in the returns section but every sentence adds value. Could be slightly tighter, but overall efficient.

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 simple two-param tool with good annotations, the description covers purpose, usage, and return structure. It lacks output schema but provides a literal return example. It's adequately complete, only missing edge cases like invalid coordinates, which are likely handled by schema validation.

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 description coverage is 0%, but the description explains the two parameters (lat, lon) with ranges and types, adding meaning beyond the schema's bare type definitions. This fully compensates for the low coverage, though it could mention error handling or precision.

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 specifies the tool's function: estimating light pollution at a given point, returning Bortle class and visibility. It differentiates from siblings by focusing on a point-specific estimate, contrasting with tools like find_dark_sky_sites or get_tonight_agenda.

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 clear use cases ('how dark is the sky at X', 'can I see the Milky Way from X') and explicitly states it's a satellite estimate, not a field measurement. However, it does not explicitly mention when not to use it or alternatives, though sibling names suggest context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Stellarhound — stargazing forecasts, dark-sky sites & aurora tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources

Need Help?