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 describes what the tool does (analyzes requirements, detects issues, generates questions) and the output format (JSON with interview need and question list), which is helpful. However, it lacks details on error handling, performance characteristics, or any constraints like rate limits or authentication needs that would be important for an analysis tool.

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 well-structured and concise. It starts with the core purpose, details the analysis process, and clearly documents the single parameter and return format in separate sections. Every sentence adds value without redundancy, and it's appropriately sized for a tool with one parameter and an output schema.

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 moderate complexity (analysis with potential question generation), no annotations, and the presence of an output schema, the description is mostly complete. It explains the tool's function, parameter semantics, and output structure. The output schema likely details the JSON format, so the description doesn't need to elaborate on return values. However, it could benefit from more behavioral context like error cases or usage prerequisites.

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 description adds significant meaning beyond the input schema. The schema only indicates 'user_input' is a required string, but the description explains it's '用戶的系統需求描述' (user's system requirement description), clarifying the expected content. With 0% schema description coverage and only one parameter, this compensation is effective, though it could specify format or length expectations.

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: '分析用戶需求並檢測是否需要蘇格拉底面試' (analyze user requirements and detect if Socratic interviewing is needed). It specifies the verb (analyze/detect) and resource (user requirements), and distinguishes from siblings by focusing on requirement analysis rather than environment checking or project delivery. However, it doesn't explicitly differentiate from validation or code-checking 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 provides no guidance on when to use this tool versus alternatives. It doesn't mention when this analysis should be performed (e.g., before validation, after initial requirements gathering), nor does it reference any sibling tools like 'mmla_validate_code' or 'record_socratic_answers' that might be related. Usage is implied but not explicitly stated.

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 describes the tool's function (environment detection) and output format (JSON report), which adds value beyond the input schema. However, it lacks details on behavioral traits such as whether it's read-only, has side effects, requires authentication, or handles errors. For a tool with zero annotation coverage, this leaves significant gaps in understanding its operational 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 concise and well-structured, with two sentences: one stating the purpose and detection scope, and another specifying the return format. It's front-loaded with the main function and avoids unnecessary details. However, it could be slightly more efficient by combining ideas, but overall, it's appropriately sized with minimal 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?

Given the tool's complexity (environment detection with no inputs) and the presence of an output schema (which handles return values), the description is reasonably complete. It covers the purpose and output format, which is sufficient for a diagnostic tool. However, it lacks details on behavioral aspects like error handling or side effects, which could enhance completeness, but the output schema mitigates some of this 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?

The input schema has 0 parameters with 100% coverage, so the schema fully documents the lack of inputs. The description adds no parameter information, which is appropriate since there are no parameters to describe. This meets the baseline of 4 for tools with zero parameters, as there's nothing to compensate for, and the description doesn't need to cover parameters.

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: to check the Bluemouse runtime environment by detecting host environment, API key configuration, and dependency status. It specifies the verb '檢查' (check) and resource '藍圖小老鼠運行環境' (Bluemouse runtime environment), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'analyze_requirement_trap' or 'mmla_validate_code', which might also involve environment assessment.

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 mentioning what it detects (host environment, API keys, dependencies), suggesting it should be used for environment validation or troubleshooting. However, it doesn't provide explicit guidance on when to use this tool versus alternatives (e.g., 'mmla_validate_code' for code validation or 'open_bluemouse_ui' for UI access), nor does it specify prerequisites or exclusions. The usage is inferred but not clearly defined.

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 full burden for behavioral disclosure. It states this is a write operation ('寫入' - write) which implies mutation, but doesn't disclose critical behavioral traits: what permissions are needed, whether files are overwritten or merged, if there are rate limits, what happens on failure, or whether this is a destructive operation. The description adds minimal context beyond the basic action, leaving significant gaps for a tool that writes files to a workspace.

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: the first sentence states the core purpose clearly. The second sentence adds conceptual context but could be considered slightly verbose. The parameter and return sections are structured but not excessively detailed. There's minimal waste, though the metaphorical language ('寄生'到『交付』' - parasitic to delivery) might not be immediately clear to an AI agent.

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 has 3 parameters with 0% schema coverage, no annotations, but has an output schema (so return values are documented elsewhere), the description is moderately complete. It covers the basic purpose and parameter meanings, which is adequate for a tool with output schema support. However, for a file-writing operation with mutation implications, it should ideally include more behavioral context (permissions, overwrite behavior, error handling) given the lack of annotations.

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 provides parameter names and basic semantics: 'project_name: 項目名稱' (project name), 'files: 文件映射 (JSON字符串)' (file mapping as JSON string), and 'metadata: 元數據 (JSON字符串)' (metadata as JSON string). This adds meaningful context beyond the bare schema, explaining what each parameter represents. However, it doesn't specify format details (e.g., JSON structure for files/metadata) or constraints, leaving some ambiguity for the 3 parameters.

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: '將生成的項目文件寫入宿主工作區' (write generated project files to the host workspace). It specifies the verb ('寫入' - write) and resource ('項目文件' - project files), though it doesn't explicitly differentiate from sibling tools like 'mmla_create_node' or 'mmla_update_status' which might also involve file operations. The metaphorical language about completing a 'parasitic to delivery' cycle adds context but doesn't detract from the core purpose clarity.

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 mentions completing a 'parasitic to delivery' cycle, which implies this is a final step in a workflow, but doesn't specify prerequisites (e.g., should 'check_bluemouse_environment' be run first?) or when to choose sibling tools like 'mmla_create_node' for creation versus this tool for delivery. There's no explicit 'when' or 'when not' guidance for the agent.

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 this is a creation tool, implying a write/mutation operation, but doesn't disclose any behavioral traits such as permissions required, whether it's idempotent, error handling, or rate limits. The mention of 'spec' as a JSON string adds minimal context but doesn't cover critical behavioral aspects for a creation tool.

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 with only two sentences that directly address the tool's purpose and a key parameter requirement. Every word earns its place—there's no redundancy or unnecessary elaboration. It's front-loaded with the main action and follows up with a specific instruction for the 'spec' parameter.

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 has 3 parameters with 0% schema coverage and no annotations, but does have an output schema, the description is moderately complete. It covers the creation purpose and one parameter's semantics, but lacks behavioral context and guidance for usage. The output schema existence means return values don't need explanation, but other gaps remain for a mutation tool with undocumented parameters.

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 adds meaning for the 'spec' parameter by explaining it should be 'a JSON string defining inputs/outputs/constraints', which clarifies its purpose beyond the schema. However, it provides no information about 'parent_id' or 'name' parameters, leaving two of three parameters with minimal semantic context. This partial compensation results in an average score.

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 'Create' and resource 'new node in the architecture', providing a specific purpose. It distinguishes from sibling tools like 'mmla_update_status' and 'mmla_validate_code' by focusing on creation rather than modification or validation. However, it doesn't specify what type of architecture (e.g., software, data, organizational) this refers to, keeping it from 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 prerequisites (e.g., needing a parent node), exclusions, or compare to sibling tools like 'analyze_requirement_trap' or 'deliver_bluemouse_project'. The only implied usage is for creating nodes, but no context on appropriate scenarios or constraints.

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?

Tool has no description.

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?

Tool has no description.

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?

Tool has no description.

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?

Tool has no description.

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?

Tool has no description.

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?

Tool has no description.

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 adds valuable context: the '🚨 核心修正 1' section specifies a critical precondition (only GREEN nodes can validate), and it describes the 'use_agentic_loop' parameter's behavior (auto-fix with up to 16 retries). This goes beyond basic validation to include retry mechanisms and access controls, though it lacks details on error handling or rate limits.

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: the core purpose is stated first, followed by a critical warning, then parameter details, and return information. Every sentence adds value, with no redundant information. However, the emoji and non-English text ('🚨 核心修正 1') might slightly hinder clarity in some contexts.

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 (validation with conditional logic and retries), no annotations, and an output schema (which handles return values), the description is fairly complete. It covers purpose, key behavioral constraints (GREEN node requirement), parameter semantics, and hints at the output ('JSON string with validation results'). It could improve by mentioning error cases or validation scope, but it's adequate for informed 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?

Schema description coverage is 0%, so the description must compensate. It adds meaning for all three parameters: 'code' is 'The Python code to validate', 'node_id' is 'The MMLA node ID to validate against', and 'use_agentic_loop' is explained with 'If True, use Agentic Loop with auto-fix (up to 16 retries)'. This provides clear semantics beyond the bare schema, though it doesn't detail formats or constraints for 'node_id'.

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: 'Validate code against MMLA specification.' It specifies the verb ('validate') and resource ('code'), and distinguishes it from siblings like 'mmla_create_node' or 'mmla_update_status' which have different functions. However, it doesn't explicitly differentiate from non-MMLA siblings like 'check_bluemouse_environment', though the MMLA focus is implied.

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 some usage context with '🚨 核心修正 1: 絕對門禁檢查 - 只有狀態為 GREEN 的節點才能進行代碼驗證', which implies prerequisites (node must be GREEN). However, it doesn't explicitly state when to use this tool versus alternatives like 'analyze_requirement_trap' or 'check_bluemouse_environment', nor does it provide exclusions or clear alternatives within the MMLA context.

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 opens a UI in a browser and returns a status and URL, but it doesn't mention critical behavioral traits such as whether this requires authentication, if it's a read-only operation, potential side effects (e.g., launching a browser session), or any rate limits. For a tool that likely initiates a user interface, this is a significant gap in transparency.

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 a clear purpose statement followed by brief parameter and return value sections. Each sentence adds value without redundancy. However, the structure could be improved by integrating usage guidelines or behavioral details more seamlessly, but it remains efficient overall.

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 (a UI-launching tool with 2 parameters and no annotations), the description is moderately complete. It explains the purpose, parameters, and returns, and an output schema exists, so return values don't need elaboration. However, it lacks behavioral context and usage guidelines, making it incomplete for safe and effective tool invocation by an AI agent.

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 description adds minimal semantics beyond the input schema. It briefly mentions that 'api_key' is optional for BYOK mode and 'mode' has values 'landing' or 'workspace', but with 0% schema description coverage, the schema itself lacks descriptions. The description compensates slightly by explaining the purpose of parameters, but it doesn't provide detailed usage examples, constraints, or default behaviors, leaving room for ambiguity.

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: '在瀏覽器中打開藍圖小老鼠的用戶界面，開始使用者旅程' (open the Blue Mouse UI in a browser to start the user journey). It specifies the verb ('打開' - open) and resource ('藍圖小老鼠的用戶界面' - Blue Mouse UI), making it clear what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'check_bluemouse_environment' or 'deliver_bluemouse_project', which might have overlapping contexts but different functions.

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 mentions starting the user journey but doesn't specify prerequisites (e.g., whether the environment must be checked first using 'check_bluemouse_environment'), exclusions, or comparisons to other UI-related tools. This lack of context leaves the agent without clear usage instructions.

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 that data is recorded to 'data_trap.jsonl' and for training data collection with user consent, but it lacks details on file handling (e.g., appending vs. overwriting), error conditions, permissions needed, or rate limits. For a tool that writes data without annotation coverage, this is a significant gap in transparency.

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: the first sentence states the core purpose, followed by usage context and parameter details. It avoids redundancy, with each sentence adding value (e.g., explaining parameters and returns). However, the structure could be slightly improved by separating usage guidelines more clearly from parameter semantics.

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 (4 parameters, no annotations, 0% schema coverage, but has an output schema), the description is moderately complete. It covers the purpose, basic usage, parameters, and return value ('記錄狀態'), but lacks behavioral details like file operations or error handling. The output schema existence reduces the need to explain returns, but without annotations, more context on data mutation and safety is needed for full completeness.

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 description lists all 4 parameters (requirement, questions, answers, framework) with brief explanations, but the input schema has 0% description coverage. The description adds some semantic context (e.g., '原始需求' for requirement, '問題列表（JSON字符串）' for questions), but it doesn't fully compensate for the schema gap—details like JSON structure, format constraints, or the meaning of 'framework' are unclear. With 4 parameters and low schema coverage, the baseline is 3 as the description provides basic but incomplete parameter info.

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: '記錄蘇格拉底面試的答案到 data_trap.jsonl' (records Socratic interview answers to data_trap.jsonl). It specifies the verb ('記錄' - record) and resource ('答案' - answers), and mentions the target file. However, it doesn't explicitly differentiate this from sibling tools like 'analyze_requirement_trap' or 'mmla_create_node', which could have overlapping data collection functions.

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: '用於訓練數據收集（如果用戶允許）' (for training data collection if the user allows). This implies a context of data collection with user consent, but it doesn't specify when to use this tool versus alternatives like 'analyze_requirement_trap' or other sibling tools. No explicit when-not-to-use scenarios 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.