calculate_tensor

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the action ('calculates') without detailing traits such as computational complexity, error handling, output format, or dependencies on state (e.g., from 'create_predefined_metric'). This leaves critical behavioral aspects unspecified.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.

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

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

Given the complexity of tensor calculations, no annotations, no output schema, and low schema coverage, the description is incomplete. It lacks details on behavior, parameter usage, and output, making it inadequate for an AI agent to effectively invoke the tool without additional context.

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

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

With 0% schema description coverage and 3 parameters, the description adds no meaning beyond the schema. It doesn't explain what 'metric_key', 'tensor_type', or 'simplify_result' represent, their expected formats, or valid values (e.g., types of tensors). This fails to compensate for the low schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool 'Calculates a tensor from a metric using einsteinpy.symbolic', which provides a verb ('calculates') and resource ('tensor from a metric'), but it's vague about what specific tensor is calculated and how it differs from siblings like 'calculate_curl' or 'calculate_divergence'. It mentions the library 'einsteinpy.symbolic' for context, but lacks specificity in distinguishing its purpose from related tools.

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

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

No guidance is provided on when to use this tool versus alternatives. With many sibling tools like 'calculate_curl', 'calculate_divergence', and 'create_custom_metric', the description fails to specify scenarios, prerequisites, or exclusions for using 'calculate_tensor', leaving the agent without context for selection.

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