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

With no annotations, the description carries full burden. It usefully discloses one real behavioral constraint (all text parameters must be supplied in English, with a translation instruction) and the test co-generation behavior, but it says nothing about permissions, output format, limits, or what happens on failure.

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?

Front-loaded with the core purpose in the first sentence, followed by two focused notes. The English/translation block is somewhat verbose but carries genuinely actionable information, so it earns its space.

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 an 8-parameter generation tool with zero schema descriptions, no annotations, and only partial parameter coverage, the description is thin. The output schema existing means return values need no explanation, but the input semantics and usage routing are largely missing.

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 0% for all 8 parameters, so the description must compensate but does not. It names a few fields (task, problem, expected_behavior, constraints, generate_test_file) only to impose the English requirement, without explaining what language, current_code, or test_cases mean or how task differs from problem.

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?

States a specific verb and resource ('Generate fixed or refactored code') and references 'Template 3' plus the coder expert model, so the agent understands the operation. However, it does not distinguish itself from siblings like generate_standard_function or generate_multi_function_module, leaving the boundary inferred.

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?

There is no explicit guidance on when to use this tool versus the sibling generators (standard function, multi-function module). The only conditional behavior mentioned is generate_test_file co-generation, which is a parameter effect rather than a when-to-use rule.

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?

With no annotations, the description carries the full burden. It does disclose that setting generate_test_file co-generates a unit test suite and that a 'coder expert model' is used, plus a notable English-only input constraint. It says nothing about how existing_code is used, permissions, latency, or failure 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 core capability is front-loaded in the first sentence, with the conditional test-generation behavior immediately after. The English-only block is verbose but earns its place as a hard invocation constraint; only the unexplained 'Template 2' reference is dead weight.

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 9-parameter, 6-required generation tool with zero schema coverage and no annotations, the description is far too thin. An output schema exists so return values need not be explained, but the caller is given no way to know what signature, existing_code, interacts_with, or test_cases should contain.

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 0% across 9 parameters, so the schema contributes no meaning and the description must compensate. It only names 'task', 'description', and 'constraints' as examples in the English-language note and explains generate_test_file; signature, existing_code, interacts_with, test_cases, and language are left entirely undefined.

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?

States a specific verb and resource ('Generate code for a function with codebase context'), so an agent knows this produces code from existing code context. However, it does not differentiate itself from the close sibling generate_standard_function, and 'Template 2' is opaque internal jargon that carries no meaning for the caller.

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 on when to choose this over generate_standard_function, generate_multi_function_module, or generate_bugfix_refactor, despite these being obvious alternatives. The only conditional behavior mentioned is the generate_test_file flag, which is not selection guidance.

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?

With no annotations, the description carries full behavioral burden. It usefully discloses the coder expert model, optional co-generated unit test suite, and mandatory English parameter translation, but omits permissions, side effects, determinism, and other operational traits.

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?

Front-loads the purpose, then states the conditional test-generation behavior, then the critical language requirement. Every sentence adds information and there is no redundant restatement.

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 10-parameter code-generation tool with no annotations, the description is incomplete. It does not explain key parameters, usage relative to siblings, or behavioral constraints beyond English translation and optional test generation, even though output-schema existence means return values need not be described.

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?

Top-level schema description coverage is 0% across 10 parameters, so the description must compensate. It only names or hints at task, module_purpose, context, functions, and generate_test_file, while language, test_cases, dependencies, shared_types, export_format, and most function-spec fields remain unexplained.

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?

States a specific verb and resource: generate code for a multi-function module (Template 4) using a coder expert model. The phrase 'multi-function module' distinguishes it from generate_standard_function, though no sibling is named explicitly.

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?

Provides no guidance on when to choose this over generate_standard_function, generate_codebase_context, or generate_bugfix_refactor. The only conditional behavior mentioned is if generate_test_file is True, which is execution behavior rather than tool selection guidance.

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?

With no annotations, the description carries the full burden. It usefully discloses that a coder expert model is used, that a test suite is co-generated when generate_test_file is true, and that all parameter values must be English. However it omits mutation/return behavior and error conditions, so the behavioral profile is only partially covered.

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?

Three short, front-loaded sentences with no filler; the English-translation requirement is flagged as IMPORTANT where an agent will see it. The parenthetical '(Template 1)' and '(such as ...)' lists are slightly loose but not wasteful.

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 tool with 11 parameters, 0% schema coverage, and no annotations, the description is materially incomplete. An output schema exists so return values need not be explained, but the vast majority of input parameters remain undefined, which is a significant gap for a code-generation tool.

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 0% across 11 parameters, so the description must compensate. It mentions generate_test_file's conditional effect, the English requirement, and lists a few field names (task, description, constraints, edge_cases), but leaves signature, language, test_cases, context, integration_note, and dependencies_allowed with no semantic explanation.

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?

States a specific verb and resource ('Generate code for a standard function spec') and names the template variant, which helps distinguish it from generate_multi_function_module and generate_bugfix_refactor. It never explicitly contrasts itself with those siblings, so the differentiation is implied rather than stated.

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 explains the conditional effect of generate_test_file but gives no guidance on when to choose this tool over generate_multi_function_module or generate_bugfix_refactor. There are no prerequisites or exclusions, leaving the selection decision to inference.

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?

With no annotations, the description carries the full burden. It usefully discloses the auto-detection behavior, the custom_command override, and that stdout/stderr are returned, but it says nothing about execution environment, timeouts, truncation, or the fact that this executes arbitrary commands with potential side effects.

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?

Three short sentences, front-loaded with the core action, and every sentence adds distinct information (what it does, why to use it, how the parameter changes behavior). No waste.

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?

The presence of an output schema means return values need not be explained, and the single optional parameter is covered. But for an execution tool with zero annotation coverage, the absence of any safety, timeout, or environment context leaves a meaningful gap.

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 0%, so the description must compensate, and it does: it explains that custom_command replaces auto-detection with the given command line. It does not clarify expected format (shell string, args, quoting), so it falls short of fully documenting the parameter.

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?

States a specific verb+resource: it auto-detects and runs the project's test suite and returns stdout/stderr. It is clearly distinct from the generation-oriented siblings (generate_*), though it never explicitly names or contrasts them.

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?

'Can be used by the AI to verify correct behavior of generated code' gives one implied usage context, which is a reasonable hint. However there is no when-not guidance, no mention of alternatives, and no prerequisites (e.g., project must be initialized, tests must exist).

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