Skip to main content
Glama

jpl_cad

Query NASA's JPL database to identify asteroid and comet close approaches to planets, enabling analysis of past and future near-Earth object events with customizable date, distance, and planetary body parameters.

Instructions

Asteroid and comet close approaches to the planets in the past and future

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dist_maxNoMaximum approach distance (e.g., 0.05, 10LD). Default: 0.05 au
dist_minNoMinimum approach distance. Default: none
date_minNoStart date for search (YYYY-MM-DD). Default: now
date_maxNoEnd date for search (YYYY-MM-DD). Default: +60 days
bodyNoBody to find close approaches to (e.g., Earth, Mars, ALL). Default: Earth
sortNoSort field: date, dist, dist-min, v-inf, v-rel, h, object. Default: date
desNoObject designation (e.g., '2000 SG344' or '433')
spkNoObject SPK-ID (e.g., '2000433')
neoNoLimit to NEOs. Default: true
fullnameNoInclude full object name in result. Default: false

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but reveals nothing about rate limits, authentication requirements, data freshness, pagination, error conditions, or response format. For a tool with 10 parameters and no output schema, this is a significant transparency gap.

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 a single, efficient sentence that states the core purpose without unnecessary words. It's appropriately sized for the tool's complexity and gets straight to the point with zero waste.

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

Completeness2/5

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

For a tool with 10 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns, how results are structured, or provide any context about data sources or limitations. The description alone leaves too many unanswered questions for effective tool use.

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?

The description adds no parameter information beyond what's already in the schema (which has 100% coverage). It doesn't explain relationships between parameters, provide examples of valid combinations, or clarify parameter interactions. With complete schema documentation, the baseline score of 3 is appropriate.

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's purpose: finding asteroid and comet close approaches to planets in past and future. It specifies the resource (asteroids/comets) and action (finding close approaches), but doesn't distinguish it from sibling tools like jpl_sentry or nasa_neo which might have overlapping functionality.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. With sibling tools like jpl_sentry (impact risk assessment), jpl_scout (NEO detection), and nasa_neo (NEO data), there's clear potential overlap, but the description offers no comparison or context for choosing this specific tool.

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