newton

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool computes derivatives, implying a read-only operation, but does not address potential errors (e.g., invalid input), performance considerations, or output format details. The description adds minimal context beyond the basic function.

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 front-loads the core purpose and includes a helpful example. Every word earns its place, with no redundancy or unnecessary elaboration, 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?

For a simple tool with one parameter and high schema coverage, the description is adequate but lacks depth. Without annotations or an output schema, it does not cover error handling, output format, or limitations, leaving gaps in understanding the tool's full behavior in more complex scenarios.

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?

Schema description coverage is 100%, so the schema already documents the 'expression' parameter with examples. The description adds marginal value by reinforcing the parameter's purpose and providing an additional example ('x^2' → '2 x'), but does not explain syntax constraints or edge cases beyond what the schema provides.

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 specific action ('Compute the derivative') and resource ('mathematical expression'), and distinguishes from sibling tools like 'integrate' and 'simplify' by focusing on differentiation. It provides a concrete example ('x^2' → '2 x') that illustrates the transformation.

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 implies usage for mathematical differentiation tasks, but does not explicitly state when to use this tool versus alternatives like 'integrate' or 'simplify'. It lacks guidance on prerequisites or exclusions, such as handling non-differentiable expressions or specifying the variable of differentiation beyond the implied 'x'.

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

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. While it describes the core function (factoring polynomials with examples), it lacks details on error handling (e.g., invalid inputs, unsupported expressions), performance characteristics, or output format beyond the examples. This leaves gaps in understanding how the tool behaves in edge cases.

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, stating the purpose clearly in the first phrase and using two illustrative examples that efficiently demonstrate the tool's functionality. Every sentence (and example) earns its place by reinforcing understanding without unnecessary elaboration.

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 low complexity (one parameter, no annotations, no output schema), the description is adequate for basic use but incomplete for robust agent interaction. It covers the core purpose and examples but lacks details on output format, error conditions, and limitations relative to sibling tools, which could hinder effective tool selection and invocation.

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 schema description coverage is 100%, with the parameter 'expression' fully documented in the schema. The description adds minimal value by repeating the parameter concept in the examples but does not provide additional syntax, constraints, or format details beyond what the schema already states. This meets the baseline for high 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 clearly states the specific action ('Factor a polynomial expression') and provides concrete examples that illustrate the transformation from input to output. It distinguishes this tool from sibling tools like 'derive', 'integrate', and 'simplify' by focusing specifically on factorization rather than differentiation, integration, or simplification.

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 implies usage through the examples (e.g., factoring quadratic expressions), but it does not explicitly state when to use this tool versus alternatives like 'simplify' or 'derive'. There is no guidance on prerequisites, limitations (e.g., polynomial degree), or exclusions, leaving usage context somewhat ambiguous.

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

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 discloses the operation (indefinite integration) and variable (x), but lacks behavioral details such as supported expression types, error handling (e.g., for invalid inputs), computational limits, or output format. The description does not contradict annotations (none exist).

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 front-loads the core purpose and includes a helpful example. Every element earns its place without redundancy or fluff, 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?

For a single-parameter tool with no annotations and no output schema, the description is adequate but has clear gaps. It covers the basic operation and parameter intent, but lacks details on behavioral traits (e.g., error cases, output structure) and does not fully compensate for the absence of structured metadata, leaving the agent with incomplete 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?

The input schema has 100% description coverage, with the 'expression' parameter fully documented. The description adds minimal value beyond the schema by reinforcing the parameter's purpose with examples ('x^2', 'cos(x)'), but does not provide additional syntax, constraints, or format details. Baseline 3 is appropriate given high 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 clearly states the tool's purpose with a specific verb ('compute') and resource ('indefinite integral of a mathematical expression'), and distinguishes it from siblings by specifying the operation (integration vs. derivation, factorization, or simplification). The example 'x^2' → '(1/3)x^3' concretely illustrates the transformation.

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 implies usage for indefinite integration with respect to x, but does not explicitly state when to use this tool versus alternatives like 'derive' (differentiation), 'factor' (factoring), or 'simplify' (algebraic simplification). No exclusions or prerequisites are mentioned.

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

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 mentions the tool 'Supports standard algebraic notation,' which adds some context about input format, but lacks details on error handling, performance limits, or output format. For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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 front-loaded and concise, consisting of two sentences that efficiently convey the tool's purpose and key feature. Every sentence earns its place by providing essential information without redundancy, making it easy to understand 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 low complexity (one parameter, no nested objects) and high schema coverage, the description is adequate but not complete. It lacks an output schema, and with no annotations, it does not fully compensate by explaining return values or behavioral traits. This results in a minimal viable description with clear gaps.

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 100% description coverage, with the 'expression' parameter well-documented in the schema. The description adds minimal value beyond the schema by providing an example ('2^2+2(2)'), but does not elaborate on syntax or constraints. Given the high schema coverage, a baseline score of 3 is appropriate.

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 with a specific verb ('simplify') and resource ('mathematical expression'), and provides an example transformation ('2^2+2(2)' → '8'). It distinguishes from siblings like 'derive', 'factor', and 'integrate' by focusing on simplification rather than calculus or factorization operations.

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 clear context for when to use this tool ('Simplify a mathematical expression') and mentions support for 'standard algebraic notation,' which helps identify appropriate inputs. However, it does not explicitly state when not to use it or name alternatives among the sibling tools, such as when factorization or integration might be more suitable.

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