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convert_anomaly

Convert orbital anomalies between mean, eccentric, and true types using eccentricity. Choose conversion name and angle format to compute the desired anomaly.

Instructions

Convert between orbital anomaly types (mean, eccentric, true).

Use list_orbital_computations() to see all available conversions.

Args: conversion: Conversion name (case-insensitive), e.g. "eccentric_to_mean". anomaly: Input anomaly value in the specified angle_format. e: Orbital eccentricity (dimensionless). angle_format: Angle unit - "degrees" (default) or "radians".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
eYes
anomalyYes
conversionYes
angle_formatNodegrees
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It mentions case-insensitivity, default angle format, but lacks details on error handling, precision, or limits. This is sufficient for a simple conversion tool but not comprehensive.

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 efficient: one sentence for purpose, a reference to list_orbital_computations(), and a clean Args block. No extraneous information.

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?

Given the tool's simplicity, the description covers the essential aspects: conversion type, input parameters, and where to find valid conversions. Lack of output schema is acceptable for a numeric conversion tool.

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?

Despite 0% schema description coverage, the Arg block explains each parameter's purpose and constraints (e.g., anomaly in angle_format, e is dimensionless). This adds significant value beyond the schema.

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 states it converts between orbital anomaly types (mean, eccentric, true). The name 'convert_anomaly' is self-explanatory, and it distinguishes from other convert tools like convert_epoch and convert_position.

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 directs users to list_orbital_computations() to see available conversions, providing guidance on valid inputs. However, it does not explicitly state when not to use this tool or compare with 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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