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 mentions the return value (path to report file) but lacks details on permissions, error handling, or whether it's read-only or destructive. This leaves significant gaps in understanding the tool's 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 brief and front-loaded with the main purpose, followed by a clear return statement. It avoids unnecessary details, making it efficient, though it could be slightly more structured by explicitly stating context or prerequisites.

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 simplicity (0 parameters, no output schema, no annotations), the description is adequate but incomplete. It explains the return value but misses behavioral context like how the report is generated or dependencies on other tools, which could help in a server with siblings like 'run_attack.'

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 0 parameters with 100% coverage, so no parameter information is needed. The description appropriately does not discuss parameters, focusing instead on the return value, which aligns well with the schema's completeness.

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 'Get the report of the last run,' which clearly indicates its function as retrieving a report. However, it lacks specificity about what type of report (e.g., attack results, system logs) and does not differentiate from siblings like 'run_attack' or 'list_garak_probes,' making it somewhat vague.

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 explicit guidance is provided on when to use this tool versus alternatives. The description implies it retrieves a report from a previous run, but it doesn't specify prerequisites (e.g., must have run an attack first) or compare to siblings like 'run_attack' for generating new reports, leaving usage unclear.

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 states the tool lists attacks and returns a list, but lacks details on permissions, rate limits, error handling, or whether the list is static or dynamic. For a tool with zero annotation coverage, this leaves significant behavioral gaps.

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 appropriately sized and front-loaded, with the core purpose stated first and return details following. Both sentences earn their place by providing essential information without redundancy. Minor improvements could include structuring the return statement more clearly, but overall it's efficient.

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 (0 parameters, no output schema), the description is minimally adequate. It covers the basic purpose and return type, but lacks context on usage, behavioral traits, or how it relates to sibling tools. Without annotations, it should provide more guidance to be fully complete.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description adds value by clarifying the return type ('list: A list of available probes / attacks'), which compensates for the lack of an output schema. This justifies a score above the baseline of 3.

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: 'List all available Garak attacks.' It specifies the verb ('List') and resource ('Garak attacks'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'list_models' or 'list_model_types', which prevents a perfect score.

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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_report' or 'run_attack', nor does it specify prerequisites or contexts for usage. The only implied usage is to retrieve attack information, but no explicit guidelines are given.

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 that the tool returns a list of strings and hints at a use case (for attack/target models), but fails to mention critical behavioral traits such as whether this is a read-only operation, if there are rate limits, authentication needs, or error conditions. For a tool with zero annotation coverage, this is a significant gap.

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 appropriately sized and front-loaded, with the core purpose stated first, followed by parameter and return details. Every sentence adds value, but the second sentence could be more integrated or omitted for tighter structure without losing clarity.

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 (1 parameter, no output schema, no annotations), the description is somewhat complete but lacks depth. It covers the basic purpose and parameter semantics but omits behavioral transparency aspects like safety or performance, which are important even for simple tools. With no output schema, it does explain the return type, which helps.

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, the description must compensate, which it does by explaining the single parameter 'model_type' with examples (ollama, openai, huggingface, ggml). This adds meaningful context beyond the schema's basic type definition, though it doesn't detail format constraints or validation rules.

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: 'List all available models for a given model type.' It specifies the verb ('list') and resource ('models'), and distinguishes it from siblings like 'list_model_types' by focusing on models within a type rather than types themselves. However, it doesn't fully differentiate from 'get_report' or 'run_attack', which are unrelated but still siblings.

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 context by stating 'Those models can be used for the attack and target models,' linking it to 'run_attack' and suggesting when this tool might be preparatory. However, it lacks explicit guidance on when to use this versus alternatives like 'list_model_types' or prerequisites for invoking it, leaving some ambiguity.

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 provided, the description carries the full burden of behavioral disclosure. It states the return type ('list[str]') and that it lists 'all available' types, which adds some context beyond the basic action. However, it doesn't cover aspects like rate limits, permissions, or error handling, which are important for a tool with no annotation coverage.

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 with the core purpose in the first sentence, followed by a clear return specification. Every sentence adds value without redundancy, making it efficient and well-structured for quick comprehension.

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 simplicity (0 parameters, no output schema, no annotations), the description is adequate but has gaps. It explains the return value, but without annotations or output schema, it could benefit from more behavioral context (e.g., is this a read-only operation, any side effects?). It meets the minimum viable standard for this low-complexity 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?

The tool has 0 parameters, and the schema description coverage is 100%, so there's no need for parameter details in the description. The description appropriately omits parameter information, earning a high baseline score for not adding unnecessary content.

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 verb ('List') and resource ('all available model types'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'list_models', which might be similar in scope, so it doesn't reach the highest score.

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 no guidance on when to use this tool versus alternatives like 'list_models' or other siblings. It lacks context about prerequisites, timing, or exclusions, leaving the agent to infer usage based on the name alone.

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 provided, the description carries the full burden of behavioral disclosure. It states the tool runs an attack and returns vulnerabilities, but lacks critical details: whether this is a destructive/mutative operation, authentication requirements, rate limits, or what 'vulnerabilities' entail (e.g., format, severity). The description covers basic behavior but misses key 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?

The description is appropriately sized and front-loaded, starting with the core purpose followed by Args and Returns sections. Each sentence serves a clear function, with no redundant information. However, the 'Args' and 'Returns' labels are slightly verbose for a short description, but overall it's efficient.

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 (running an attack with 3 parameters), no annotations, and no output schema, the description is incomplete. It doesn't explain the attack process, potential side effects, error handling, or the structure of the returned vulnerabilities list. For a tool with significant operational implications, this leaves too many unknowns.

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. It lists the three parameters with brief explanations (e.g., 'model_type (str): The type of model to use'), but these add minimal semantic value beyond the schema's titles. No examples, constraints, or relationships between parameters (e.g., valid model_type values) are provided, leaving significant gaps.

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 'Run[s] an attack with the given model and probe which is a Garak attack', which provides a basic verb+resource (run attack with model/probe). However, it's vague about what 'attack' means in this context (e.g., security testing, adversarial prompting) and doesn't distinguish it from sibling tools like 'get_report' or 'list_garak_probes'. The purpose is understandable but lacks specificity.

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. It doesn't mention prerequisites (e.g., needing to list models/probes first), when not to use it, or how it relates to sibling tools like 'get_report' for results. The description implies usage through parameters but offers no contextual advice.

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