gradient_descent

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 states the tool performs gradient descent to find a minimum, implying iterative optimization, but doesn't describe key behaviors like convergence criteria (e.g., based on tolerance or max_iterations), error handling (e.g., for non-convex functions), output format, or performance characteristics (e.g., computational cost). This is inadequate for a tool with 6 parameters and no output schema.

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 extremely concise and front-loaded: a single sentence that directly states the tool's purpose, followed by domain and category in parentheses. Every word earns its place with no redundancy or fluff, making it easy for an agent 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 tool's complexity (numerical optimization with 6 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't cover parameter meanings, behavioral details (e.g., iteration limits, convergence), or return values. While conciseness is high, it sacrifices necessary context for effective tool use.

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?

The input schema has 6 parameters with 0% description coverage, meaning none are documented in the schema. The description adds no parameter semantics—it doesn't explain what 'f', 'grad_f', 'x0', 'learning_rate', 'max_iterations', or 'tolerance' represent, their expected formats (e.g., 'f' as a string expression), or typical values. This leaves parameters completely undocumented, failing to compensate for the schema gap.

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 clearly states the tool's purpose: 'Perform gradient descent optimization to find minimum of a function.' It specifies the verb ('Perform gradient descent optimization'), resource ('function'), and scope ('find minimum'), and distinguishes it from sibling tools like 'coordinate_descent' and 'gradient_descent_momentum' by not mentioning momentum or coordinate-specific methods. However, it doesn't explicitly differentiate from 'nelder_mead' (another optimization tool), so it's not a perfect 5.

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?

The description provides minimal usage guidance. It mentions the domain ('numerical') and category ('optimization'), which implies when to use it, but doesn't specify when to choose this over alternatives like 'gradient_descent_momentum', 'coordinate_descent', or 'nelder_mead'. No exclusions or prerequisites are stated, leaving the agent with little practical direction.

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