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 fails to disclose behavioral traits such as whether the tool is read-only, requires permissions, or has side effects. It only lists what it analyzes, not how it behaves.

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 with a clear one-sentence purpose and a structured argument list. It is not overly verbose, though the argument descriptions could be slightly more detailed.

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 output schema exists, the description does not need to explain return values. However, it lacks context on the analysis process, results format, or limitations, making it adequate but not 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% description coverage, but the description explains each parameter: 'code' as code content, 'language' with allowed values, and 'file_path' as optional for reporting. This adds essential meaning beyond the bare schema types.

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: analyzing application code for GDPR compliance issues, listing specific examples like missing consent checks and PII logging. It distinguishes itself from sibling tools which cover other GDPR aspects like data flow or infrastructure.

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 on when to use this tool versus alternatives. The description implies it's for application code (vs. infrastructure code), but does not mention when not to use it or provide comparisons to siblings.

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 exist, so description must disclose behavioral traits. It only lists what it assesses without mentioning side effects, authentication, rate limits, error handling, or output behavior. Minimal behavioral disclosure.

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?

Description is concise and structured: one-sentence purpose, bullet list of assessments, then parameter docs. No wasted words.

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 specialized nature and the presence of an output schema, the description covers purpose, parameters, and assessments adequately. Minor gap: no mention of typical return values or results, but output schema likely handles that.

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 description fully compensates by providing clear meaning for each parameter: code as application code content, language with examples (python, typescript, etc.), and file_path as optional reporting path.

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?

Description clearly states it analyzes code for breach notification readiness under GDPR Art. 33-34, and lists specific assessment areas (logging, alerting, incident tracking, etc.). This distinguishes it from siblings like analyze_application_code and analyze_code_ast.

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 does not explicitly state when to use this tool versus alternatives. Usage is implied through GDPR context, but no 'when not to use' or comparative guidance is provided.

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 provided, so description carries full burden. It details how AST works (filters comments/strings, tracks variables, identifies functions, verifies semantic intent) and lists detections. Lacks mention of potential side effects or limits, but sufficient.

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?

Well-structured with a lead sentence, bullet points for benefits and detections, and an Args list. Every sentence adds value, though slightly lengthy; could be more concise 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?

Output schema exists, so return values not needed. Description covers all parameters, use cases, and behavior. Complete for a complex analysis 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 coverage is 0%, but description includes an Args section explaining each parameter's purpose (code, file_path, language, deep_analysis), adding meaning beyond schema titles and types.

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?

Clearly states 'Analyze code using AST for GDPR compliance' with specific languages (Python, JavaScript, TypeScript). Distinct from siblings like 'analyze_application_code' which likely uses different methods.

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?

Explains when to use AST over regex (higher accuracy, reduces false positives, tracks data flow) and lists specific detections. Does not explicitly mention when not to use or compare to direct siblings, but context is sufficient.

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?

The description explains the tool detects patterns in code but does not disclose any behavioral traits such as performance, limitations, or absence of side effects. With no annotations, the description carries the burden; it provides minimal behavioral insight beyond the operation.

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 two paragraphs plus an argument list, compact and to the point. Every sentence adds value, and the format is front-loaded with the core purpose followed by examples and argument details.

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 output schema exists, the description does not need to explain return values. It provides a clear purpose and lists patterns detected, which gives sufficient context. However, it does not mention what form the analysis results take (e.g., classification or list), but this is minor given the schema.

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 add meaning. It explains that 'code' is the source code to analyze and 'language' is the programming language with examples. This adds value beyond the schema, though it does not enumerate all possible languages or specify format constraints.

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 it analyzes source code for controller vs processor role patterns, listing specific indicators like direct user data collection and consent mechanisms. This makes the tool's purpose distinct from sibling tools like assess_controller_processor_role or analyze_application_code.

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 when source code is available but provides no explicit guidance on when to use this tool versus alternatives like assess_controller_processor_role or when not to use it. No prerequisites or exclusions 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 provided; the description discloses it analyzes code and provides guidance but does not clarify behavioral traits such as whether it modifies data, requires authentication, or has 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?

Well-structured with a purpose sentence, bulleted detections, and parameter list; concise but could be slightly more front-loaded.

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 presence of an output schema and 3 parameters, the description covers purpose, detections, and guidance, though it omits mention of output format or potential limitations.

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 adds parameter descriptions (code, language, file_path) but they are brief and lack specifics like accepted language values or code format.

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 it analyzes code for GDPR Chapter V cross-border transfers, lists specific detections, and distinguishes from sibling tools like analyze_data_flow by focusing on cross-border aspects.

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 when analyzing code for cross-border transfers but lacks explicit guidance on when to use versus alternatives like analyze_data_flow or assess_controller_processor_role.

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 bears full responsibility. It describes what the tool analyzes (data flow patterns) but omits behavioral details such as whether it is read-only, permissions required, rate limits, or any side effects. The agent cannot infer safety or constraints.

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 relatively concise with clear bullet points and an 'Args:' section. One or two sentences could be trimmed without losing meaning, but it is well-structured and front-loaded with the main purpose.

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 presence of an output schema, the description does not need to explain return values. It covers the essential purpose and the lifecycle stages. However, it lacks details on prerequisites or analysis method, which would be helpful. Still, it is fairly complete for a straightforward analysis 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?

With 0% schema description coverage, the description adds meaning by explaining 'code' as application code content, 'language' with examples, and 'file_path' as optional for reporting. However, it does not provide allowed values or constraints (e.g., supported languages), leaving some 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 'Analyze code for personal data flow patterns to support ROPA documentation' and details the mapping of data lifecycle stages (Collection, Storage, Transmission, Deletion). This distinguishes it from sibling tools like analyze_application_code or analyze_breach_readiness, which have different focuses.

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 the tool is for GDPR compliance touchpoints under Article 30 ROPA, but it does not explicitly state when to use it versus alternatives, nor does it provide exclusions or usage contexts. With many sibling tools, more guidance would help the agent select correctly.

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 full burden. It only states 'detects patterns' but does not disclose read-only behavior, required permissions, rate limits, or side effects. The description is insufficient for behavioral 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 well-structured with a main line, a bullet list of articles, and an Args section. It is front-loaded with the purpose. The article list is slightly lengthy but relevant; overall it is efficient and clear.

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?

An output schema exists, so return values need not be detailed, but the description still misses usage guidance and behavioral traits. It adequately covers purpose and parameters, so it is somewhat complete for a tool of this complexity.

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 coverage is 0%, so the description must compensate. The Args section adds brief descriptions for all three parameters (code, language, file_path) and gives examples for language. However, it does not specify constraints or formats beyond basic 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?

The description clearly states the tool analyzes code for DSR implementation capabilities and explicitly lists specific GDPR articles (Art. 15-22). This distinguishes it from sibling tools like analyze_breach_readiness or analyze_data_flow, which focus on other aspects.

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 does not provide guidance on when to use this tool versus alternatives. It lacks explicit 'when to use' or 'when not to use' instructions, making it hard for the agent to choose among many GDPR analysis siblings.

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?

Description lists compliance checks (data residency, encryption, etc.) but does not disclose side effects, authentication requirements, or output format. No annotations are present, so the description carries full burden but covers only partial behavioral context.

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?

Two clear paragraphs with an Args list. Front-loaded purpose, no fluff. Every sentence adds value.

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?

Description covers tool purpose and parameter semantics. Given the presence of an output schema (not shown) and no annotations, it provides a solid baseline. Could better differentiate from many sibling tools but is largely sufficient.

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 compensates by providing meaningful explanations for all parameters: code (IaC content), file_type (with possible values), file_path (optional for reporting), and context (optional). This adds semantic value beyond the bare schema.

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?

Description clearly states the tool analyzes Bicep/Terraform/ARM code for GDPR compliance, specifying the resource types and compliance areas. This distinguishes it from siblings like analyze_application_code.

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 when to use the tool (when analyzing IaC code for GDPR compliance) but does not explicitly exclude other scenarios or contrast with similar tools like analyze_application_code. No 'when not to use' guidance is 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 must fully disclose behavioral traits. It states the tool 'assesses' and 'analyzes', implying it is read-only, but it does not explicitly confirm non-destructive behavior, required permissions, or potential side effects. The description lacks transparency about what happens to the input or system state.

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 relatively concise: a clear one-sentence summary followed by a brief explanation and the parameter description. The Args section adds value without being overly verbose. It loses a point for minor redundancy (the second sentence partially restates the first) but is generally 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?

The tool has a single parameter, an output schema (not shown), but no sibling or annotation context. The description covers the input well but does not mention what the tool returns (the role classification). Since an output schema exists, the burden is lower, but the description would benefit from a brief note on output format.

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 zero description coverage for the single parameter, so the description must compensate. It does so by detailing that 'service_description' should include the service, data flows, business relationships, and processing activities. This adds significant meaning beyond the schema's simple title and type.

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 assess whether a service/system acts as a data controller, processor, joint controller, or mixed role under GDPR. It uses a specific verb (assess) and a concrete resource (GDPR role), and the role is distinct from sibling tools like analyze_data_flow or get_role_obligations.

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 that the tool analyzes service descriptions against GDPR definitions and EDPB guidance, which implies its usage context. However, it does not explicitly state when to use this tool versus alternatives, nor does it provide exclusion criteria or prerequisites. The guidance is adequate but not explicit.

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 must disclose behavioral traits. It only states the action without mentioning side effects, authorization needs, or rate limits. The tool appears read-only but this is not confirmed.

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 two sentences. The first sentence clearly states the tool's purpose, and the second explains the parameter. No unnecessary words.

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 and the presence of an output schema, the description is mostly adequate but lacks guidance on usage context and behavioral details. It does not explain what the assessment result looks like or when to use it.

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 has 0% description coverage for the parameter. The description adds minimal semantic value by calling it 'Free-text description of the data processing', which clarifies the input but lacks format or constraints.

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 assesses whether a DPIA is required for a processing activity. It distinguishes from sibling tools like generate_dpia_template and get_dpia_guidance by focusing on the assessment of need.

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 such as get_dpia_guidance or generate_dpia_template. The description only states the function without context on prerequisites or exclusions.

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 must disclose behavioral traits. It fails to specify whether the tool is read-only, requires permissions, or has side effects. The existence of an output schema is noted but not described.

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 core purpose. The args list is slightly redundant with the schema but not overly verbose. No fluff.

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 an output schema, the description covers purpose and parameter meaning but lacks usage guidelines and behavioral context, which are 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?

With 0% schema description coverage, the description adds minimal meaning: it clarifies the parameter accepts a 'description of the retention policy', but lacks format, length, or example guidance. A single string parameter lowers the need, but more detail would help.

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 assesses a data-retention policy against specific GDPR principles (storage-limitation and right to erasure) with article references, differentiating it from sibling tools like assess_controller_processor_role.

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 use this tool over others, no prerequisites, no exclusions or alternatives mentioned. The description only states the function without context for tool selection.

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 present, so the description must convey behavioral traits. It implies a read-only analysis ('Check') but does not explicitly state that the tool is non-destructive or whether permissions are needed. The behavioral disclosure is minimal.

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 very short, with one sentence and an args line. It is front-loaded with the primary purpose, but could be more structured. No wasted sentences.

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 single simple parameter and the presence of an output schema, the description is minimal. It does not specify what the output contains or how to interpret results, leaving the agent with limited context beyond the basic functionality.

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 a brief explanation of the 'system_context' parameter ('Description of the system and data it holds'), which adds basic meaning but lacks details on expected format, examples, or constraints.

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 checks deletion/anonymization capabilities required by GDPR, using a specific verb and resource. However, it does not distinguish it from sibling tools like analyze_dsr_capabilities, which may have overlapping purpose.

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. The description simply states what it does, omitting any context about prerequisites, exclusions, or scenarios where other tools are more appropriate.

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 exist, so the description carries the full burden. It discloses that the tool generates a checklist with specific content (mandatory/recommended clauses, Azure considerations), but does not state whether this is a read-only operation, any side effects, permissions needed, or limitations. This is moderate 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 extremely concise: two short sentences plus a compact parameter description. It is front-loaded with the core purpose and adds details efficiently without any superfluous content.

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 an output schema (not shown), the description explains the input and output nature (checklist with specific content). It is complete enough for an agent to understand what the tool does and what to provide, though it could mention the output format or any return schema details.

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 coverage is 0%, meaning no parameter descriptions in the schema. However, the description includes an 'Args:' section that explains the 'context' parameter should be a 'description of the processing relationship and context', which adds meaning beyond the raw schema type. This partially compensates for the lack of schema descriptions.

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 generates an Article 28 DPA checklist with mandatory/recommended clauses and Azure-specific considerations. It uses a specific verb ('Generate') and resource ('checklist'), and the purpose is distinct from sibling tools like generate_dpia_template or generate_ropa_template.

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 (e.g., generate_dpia_template). The description does not specify when-not to use it, prerequisites, or typical scenarios, leaving the agent to infer context from the tool name and description 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?

No annotations are provided, so the description carries full burden for behavioral disclosure. It states the tool 'generates' a template, implying a read-like operation, but does not explicitly confirm safety (e.g., no side effects, no data stored). It lacks details on whether it requires authentication, uses external APIs, or has any 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 exceptionally concise: two sentences plus an 'Args' line. Essential information is front-loaded with no redundancy. Every sentence contributes value.

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 simplicity (one parameter) and presence of an output schema, the description is largely complete. It covers the input and the template's content (guidance, risk assessment, mitigation). However, it could be slightly improved by hinting at the output format or structure (e.g., 'returns a pre-filled document').

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 sole parameter 'processing_description' has 0% schema description coverage. The description adds 'Free-text description of the data processing', which provides basic meaning beyond the schema title. However, it does not specify length limits, expected detail, or formatting hints, so it only minimally compensates for the lack of schema documentation.

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 'Generate a DPIA template' with a specific verb and resource. It distinguishes itself from siblings like 'assess_dpia_need' and 'get_dpia_guidance' by focusing on template generation rather than assessment or guidance retrieval.

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 when a processing description is available ('for the described processing activity'), but does not provide explicit guidance on when to use this tool versus alternatives such as 'get_dpia_guidance' or 'assess_dpia_need'. 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 bears full responsibility. It only states the generation action without disclosing side effects, idempotency, authorization needs, or any behavioral traits. The agent is left guessing about 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 extremely concise: two sentences plus a parameter list. It is front-loaded with the core purpose and includes no extraneous information. Every element earns its place.

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 output schema exists, return values are covered. The two parameters are documented. However, the tool lacks usage guidelines and behavioral transparency, making it feel incomplete for a workflow generator that likely has a complex process.

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 a brief explanation for each parameter: request_type as 'Type of DSR' and system_context as 'Optional description of the system architecture'. While minimal, this provides basic semantics beyond the schema's titles.

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 generates a step-by-step DSR fulfilment workflow with Azure implementation notes. This verb+resource combination is specific and distinct from sibling tools like get_dsr_guidance or get_dsr_timeline.

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. The description does not mention context, prerequisites, or exclude scenarios, leaving the agent without decision support.

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 full burden. It only says 'Generate a template', which is a creation action, but gives no indication of side effects, idempotency, or other behavioral 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 a single focused sentence with a docstring, no wasted words. It is front-loaded with the core purpose.

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 output schema exists, the description does not need to explain return values. However, it lacks details about the template structure or any prerequisites, making it minimally complete for a tool with one parameter.

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%, but the docstring explains that 'organization_context' should describe the organization, role, and main processing activities. This adds meaning beyond the bare schema, though format or examples are missing.

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 explicitly states 'Generate a Records of Processing Activities (ROPA) template per Art. 30', using a specific verb and resource. This clearly distinguishes it from sibling tools like 'validate_ropa' or 'get_ropa_requirements'.

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 use this tool versus alternatives such as 'validate_ropa' or 'create_ropa'. The description only states what it does, not the conditions for use.

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?

Without annotations, the description carries full burden. The verb 'get' implies a read operation, but it does not explicitly state safety, authentication needs, or what 'context' means. It is minimally adequate for a simple retrieval 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 a single, efficient sentence with no wasted words. It is appropriately sized and front-loaded.

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 existence of an output schema, the description is still too brief. It does not specify how to format the article ID or what 'context' includes, which is important for correct invocation among related tools.

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 coverage is 0%, so the description must compensate for the parameter. It does not mention 'article_id', leaving the agent to infer its meaning from the name alone. The schema itself provides basic info, but the description adds no value.

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 retrieves the full text and context of a specific GDPR article, distinguishing it from siblings like 'get_recital' and 'search_gdpr'. However, it does not explicitly mention the need for an article ID.

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 such as 'get_recital' or 'search_gdpr'. The description lacks usage context and exclusions.

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 must cover behavioral transparency. It adequately describes a read-only operation with no side effects, but does not mention authentication, rate limits, or any other behavioral 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 two concise sentences, front-loading the purpose and listing return items efficiently without any fluff.

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 no parameters and an existing output schema, the description provides complete context about what the tool returns, leaving no gaps for an agent to infer.

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 no parameters, schema coverage is 100%, baseline is 3. The description adds value by detailing the output structure (supported languages, etc.), exceeding the baseline for zero-parameter tools.

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 retrieves information about AST analysis capabilities, listing specific return items (supported languages, analysis categories, etc.). It distinguishes from sibling tools like 'analyze_code_ast' by focusing on static capabilities rather than actual analysis.

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 querying capabilities before performing analysis, but lacks explicit guidance on when to use this tool versus alternatives, such as when to call analyze_code_ast instead.

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, and the description lacks any behavioral traits such as error handling, read-only nature, or rate limits. The minimal description fails to compensate.

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?

Single sentence, 10 words, no redundancy. Efficient but could include more detail without harming conciseness.

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?

With an output schema present, return values don't need explanation. However, input format and potential errors are not addressed, making it adequate but not 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?

Schema coverage is 0%, so the description must add meaning. It clarifies that article_id refers to a GDPR article, but does not specify format (e.g., 'Article 17' vs '17'), leaving 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?

Description clearly states the verb 'Get', the resource 'Azure service recommendations', and the context 'mapped to a specific GDPR article', making it distinct from siblings like get_article or get_dpia_guidance.

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 on when to use this tool versus alternatives. Among many GDPR-related siblings, no context is provided for appropriate usage.

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 provided. Description only states purpose without disclosing behavioral traits like return format, read-only nature, or side effects. For a read tool, minimal 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?

Single sentence with no redundant words. Front-loaded with purpose, 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 low complexity, single parameter, and presence of output schema, description is nearly complete. Could mention that it returns the definition text, but not essential.

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 coverage, description adds context that 'term' should be from Art. 4. However, no format, allowed values, or examples. Moderately compensates for missing schema descriptions.

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?

Description clearly states verb 'Get' and resource 'GDPR definition for a specific term (Art. 4)'. It distinguishes from siblings like 'search_definitions' and 'list_definitions' by specifying a single term and referencing Article 4 of GDPR.

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?

Implies use for single term definitions from Art. 4 but lacks explicit when to use vs. siblings. No exclusions or alternative guidance provided.

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 provided, and the description does not disclose any behavioral traits (e.g., read-only, auth needs, rate limits). The description bears full burden but only states the purpose.

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?

Two concise sentences with examples. Purpose is front-loaded. No wasted words.

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?

Low complexity tool with one parameter and output schema. Description is adequate but does not specify return format or differentiate behavior from siblings.

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 coverage is 0%, but the description provides example values for the 'topic' parameter. However, it lacks details on allowed values, format, or constraints beyond examples.

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?

Description clearly states the tool retrieves DPIA guidance for a specific topic, with examples. Differentiates from sibling tools like 'assess_dpia_need' and 'generate_dpia_template'.

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 use this tool versus alternatives such as 'assess_dpia_need' or 'generate_dpia_template'. The description does not mention prerequisites or exclusions.

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 provided; the description suggests a read-only operation ('Get guidance') but does not disclose authentication needs, side effects, or other behavioral traits beyond the obvious.

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 very short and front-loaded, but effectively conveys the tool's purpose and parameter options. Could be slightly improved with a brief note on output.

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 (one parameter, output schema present), the description is fairly complete. It explains the parameter's accepted values, though it could mention that the guidance is GDPR-specific.

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 meaning to the sole 'request_type' parameter by listing valid values ('access', 'rectification', etc.), compensating for the schema's lack of enum constraints (0% 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 verb 'get' and resource 'guidance on handling a specific data-subject request,' distinguishing it from siblings like 'generate_dsr_workflow' and 'get_dsr_timeline'.

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 when guidance on a specific DSR type is needed, but provides no explicit when-to-use or when-not-to-use guidance compared to alternative sibling tools.

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 bears full responsibility. It only states the basic function (read operation) but fails to disclose behavioral traits like authentication requirements, data freshness, 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 very brief with two lines, no unnecessary words. However, it could be better structured with examples or value constraints.

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?

With an output schema present, return values need not be explained, but the input parameter lacks detail. For a single-parameter tool, providing valid request_type values or examples would complete the context. The description is incomplete.

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 coverage is 0%, and the description adds 'Type of DSR' for request_type, which is vague. No enum, examples, or further clarification on valid values, leaving the agent with insufficient information.

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 retrieves GDPR-mandated response timelines and extension rules for a DSR type, specifying the action and resource. It distinguishes from siblings like get_dsr_guidance and analyze_dsr_capabilities by focusing on timelines.

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 use this tool versus alternatives such as get_dsr_guidance or analyze_dsr_capabilities. The description implies it is for checking specific legal deadlines, but no explicit context or exclusions are provided.

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 bears full responsibility. It only notes the action (get) without detailing behavior on missing recital numbers, error handling, or output format. The description is minimally transparent.

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, front-loaded sentence with no extraneous words. It is appropriately concise.

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 output schema exists to document return values, the description covers the basic purpose. However, it does not explain what a recital is or how it differs from articles/definitions, which could help the agent decide when to use this tool. Slight gap in 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 input schema has 0% description coverage, and the tool description adds no information about the recital_number parameter, such as expected format (e.g., integer vs. string) or value constraints. The description fails to compensate for the schema's lack of detail.

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 retrieves the text of a specific GDPR recital, using a specific verb and resource. It implicitly distinguishes from sibling tools like get_article and get_definition by focusing on recitals.

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 select this tool over alternatives such as get_article or get_definition. The description does not mention context or prerequisites for using the tool.

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 disclosing behavioral traits. It does not mention that the tool is read-only, what happens if an invalid data_category is provided, or any authentication or rate-limiting considerations.

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 short and front-loaded with the purpose. The 'Args' block repeats parameter info but is acceptable. It could be improved by integrating parameter details more concisely.

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 lookup tool with one parameter and an output schema (not shown), the description is adequate for basic understanding but lacks behavioral transparency and usage context. It does not clarify the tool's read-only nature or error handling.

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 coverage is 0%, so the description must compensate. The 'Args' section lists example values but does not provide constraints, format, or allowed values beyond examples. This adds minimal semantic value over the raw schema.

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 action ('Get') and the resource ('GDPR-aligned retention guidance') and scopes it to a specific data category, distinguishing it from sibling tools like 'assess_retention_policy' and 'check_deletion_requirements' which focus on policy assessment or deletion checks.

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 alternatives such as 'assess_retention_policy' or 'get_dpia_guidance'. No preconditions, context, or exclusions are provided.

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 provided, but the description explains the return content (obligations from GDPR articles with optional Azure guidance) and parameter effects, sufficient for a read-only 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 concise with a clear header and structured Args section, no redundant sentences.

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?

With an output schema present, the description adequately covers the tool's purpose and parameters, needing no further behavioral details.

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 coverage is 0%, but the docstring enumerates allowed values for 'role' and explains 'include_azure' beyond the schema's type/default, adding critical meaning.

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 'Get' and resource 'GDPR obligations' specific to a role, distinguishing it from siblings like get_article or get_role_scenarios.

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 role-specific obligations but lacks explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives among many sibling tools.

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 full weight. It states it returns 'typical scenarios' and 'guidance,' but does not disclose behavioral traits like read-only status, side effects, pagination, or output format. The return value structure is not described beyond a high-level list.

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 with two paragraphs: a two-sentence purpose and a brief parameter explanation. It is front-loaded but could be slightly more streamlined by separating parameter info from the main description. No unnecessary repetition.

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 that an output schema exists, the description does not need to detail return values. It covers the parameter adequately and sets expectations for the content (scenarios, role classification, exceptions). However, it does not explain the format of exceptions or how guidance is structured, leaving some gaps for a tool with no 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?

The only parameter 'scenario_type' has a default and title in schema but no description. The description adds meaning by listing example values ('saas', 'api', 'cloud') and clarifies that 'all' returns all scenarios. This compensates well for the schema's lack of description, providing actionable guidance.

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?

Description states 'Get common controller/processor scenarios and role determinations,' which clearly identifies the verb and resource. It specifies it returns typical scenarios with role classification and exceptions. While it differentiates from siblings like 'assess_controller_processor_role' implicitly, it does not explicitly distinguish its purpose from similar tools.

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 on when to use this tool vs alternatives such as 'assess_controller_processor_role' or 'get_role_obligations.' The description implies usage for getting typical scenarios but lacks when-not-to-use or context for selection.

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 full burden. The docstring states the behavior (returning mandatory fields based on role) and the argument type, implying a read-only operation. It could explicitly note that no data is modified, but 'get' strongly suggests non-destructiveness.

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 two concise sentences, front-loading the purpose and efficiently covering the main parameter behavior. No unnecessary words.

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 (one parameter, no nested objects) and the presence of an output schema (not shown), the description is mostly complete. It could optionally hint at the output format (e.g., 'returns a list of fields'), but the existing detail suffices for agent 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?

With 0% schema description coverage, the description adds significant value by explaining that role must be 'controller' or 'processor' and determines required fields. This clarifies the only parameter's semantics beyond the schema's type and default.

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 'Get the mandatory ROPA fields for a given organizational role,' specifying the verb, resource, and key parameter (role). It provides permissible values, distinguishing it from siblings like generate_ropa_template or validate_ropa.

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 that the role parameter determines required fields ('controller' or 'processor'), giving clear context for when to invoke it. However, it does not explicitly contrast with sibling tools (e.g., when to use get_ropa_requirements vs. generate_ropa_template), though the distinction is implicit.

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 burden for behavioral disclosure. It only states it lists articles, but fails to mention if it's read-only, returns a list, handles pagination, or any error scenarios. The description adds minimal transparency beyond the tool's 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, clear sentence with no extraneous words. However, it may be too brief; some additional context could be added without harming conciseness.

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 and presence of an output schema, the description is adequate but lacks detail on the parameter format and usage context. For a list operation with many sibling tools, more guidance on distinguishing use cases would improve 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 single 'chapter' parameter has no description in the schema (0% coverage). The description only adds 'specific GDPR' context, which is trivial given the tool name. It does not clarify acceptable values (e.g., '1' vs 'I' vs 'Chapter 1'), leaving ambiguity for the agent.

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 lists articles within a GDPR chapter, using a specific verb ('List') and resource ('articles within a specific GDPR chapter'). It distinguishes itself from siblings like 'get_article' which retrieves a single article, and 'list_definitions' which lists definitions.

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 its many siblings, nor does it mention any prerequisites or limitations. For example, it doesn't clarify how the chapter parameter should be specified (e.g., number or name) or when to prefer this over searching articles.

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 must convey behavioral traits. It indicates a read-like operation ('list all') with no side effects, but does not mention authentication, rate limits, or behavior if no definitions exist. It is minimally adequate but lacks depth.

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, complete sentence that conveys the essential information without extraneous words. It is perfectly concise and front-loaded with the key action and resource.

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 (zero parameters, straightforward purpose) and the presence of an output schema, the description is fully complete. It covers what the tool does and from where, with no missing 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 no parameters and 100% description coverage (trivially). The description provides no additional parameter meaning because none is needed. Baseline is 3, but since the tool has no parameters, a score of 4 is appropriate as the description does not need to compensate.

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 action (list), resource (GDPR definitions), and specific source (Article 4). It naturally differentiates from sibling tools like get_definition (single definition) and search_definitions (search), fulfilling the requirement of a specific verb+resource with sibling distinction.

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 retrieving all definitions from Article 4, but does not explicitly state when to use this tool versus alternatives like search_definitions or get_definition. No exclusion criteria or context are provided, so guidance is merely implied.

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, and the description does not disclose any behavioral traits such as output format, pagination, or required permissions. It only indicates a search operation.

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?

Single sentence, no excess words, front-loaded with essential information. Efficient and to the point.

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?

While an output schema exists (reducing need to describe returns), the description doesn't address potential use cases or limitations, leaving some context ambiguous.

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 meaning to the 'keywords' parameter by stating the search is by keywords, but it lacks details on format or allowed syntax, and schema description coverage is 0%.

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 searches GDPR definitions using keywords, distinguishing it from 'get_definition' (specific definition) and 'list_definitions' (all definitions).

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 use this tool versus alternatives like 'get_definition' or 'list_definitions'. The description only states the action without 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 must disclose behavior. It implies a read-only operation but does not mention safety, return format, or pagination. However, the presence of an output schema reduces the burden of explaining return values.

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, front-loaded sentence of 8 words with no redundant information. Every word earns its place.

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 and the existence of an output schema, the description is largely complete. It could still benefit from a brief note on typical use cases or limitations, but it suffices for a basic search 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?

With 0% schema description coverage, the description should compensate. It links the 'keywords' parameter to the search action but provides no details on format, specificity, or interpretation (e.g., exact match, case sensitivity). This adds minimal value beyond the schema.

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 searches across GDPR articles and recitals using keywords, specifying the verb 'Search', the resource 'GDPR articles and recitals', and the method 'by keywords'. This distinguishes it from sibling tools like get_article and get_recital which retrieve specific items.

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 such as search_definitions or get_article. With many sibling tools, the absence of usage context is a significant gap.

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. The description lists what the tool checks but does not explicitly state if it is read-only or has side effects. It adds some value beyond schema but lacks full behavioral disclosure.

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 with a clear summary sentence, a bullet list of checks, and a structured Args section. No redundant information.

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 validation tool with three parameters and an output schema, the description covers purpose, checks, and parameter semantics. It does not explain return values but the output schema exists. Minor gap: could mention it is a read-only check.

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 defines all three parameters: code, file_type (with example values), and strict_mode (default true). This adds significant meaning beyond the raw schema.

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 'Validate IaC configuration against GDPR mandatory requirements' and lists specific checks. The verb 'validate' with 'IaC configuration' is specific, and the tool is distinguished from sibling analysis tools by its focus on GDPR compliance.

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 includes 'BEFORE deploying', providing clear context. It implies usage before deployment but does not explicitly mention when not to use or alternatives among siblings.

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 full burden. It only states 'Validate', implying a read-only operation, but does not disclose whether it modifies data, requires permissions, or what the output format is. Minimal behavioral insight.

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 very concise, consisting of a single purpose line and a parameter line. It is well-structured and easy to parse, though it sacrifices depth.

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 that an output schema exists but is not shown, the description need not detail return values, but it fails to explain what validation entails (e.g., returns errors, passes/fails). It lacks completeness for a validation 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 parameter description 'The ROPA content to validate (text/JSON/markdown)' adds useful format details beyond the schema's bare type 'string'. This helps the agent prepare input correctly.

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 validates a ROPA document against Art. 30 mandatory fields. This is a specific verb-resource combination that differentiates it from siblings like 'generate_ropa_template' or 'get_ropa_requirements'.

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, nor does it mention prerequisites or limitations. Without context, the agent may misuse it.

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