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

No annotations are provided, so the description carries the burden. It explains the tool's behavior (checks input flags and returns violations with penalties and recommendations) and adds useful context about the penalty tier. It does not detail side effects (e.g., read-only, data retention), but for a check tool this is less critical and the return behavior is disclosed.

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-organized with Args and Returns sections, front-loads the key purpose, and includes only the penalty context needed for urgency. Every sentence earns its place with no redundancy.

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 8 optional parameters, an output schema exists, and no annotations, the description fully defines all inputs and what to expect in return. It provides enough context for an agent to select and invoke the tool correctly without over-explaining.

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, but the description explains every one of the 8 boolean parameters with clear meaning (e.g., 'uses_subliminal_techniques: Manipulates behavior via subliminal techniques'). This adds essential value beyond the raw schema, which only provides names and defaults.

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: 'Check if AI system violates prohibited practices under Article 5.' This is a specific verb and resource, and it distinguishes from siblings like classify_ai_system_risk or determine_eu_ai_act_role by focusing narrowly on Article 5 prohibited practices.

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 gives clear context: this tool checks for Article 5 violations, which are the highest-penalty offenses. However, it does not explicitly contrast with alternatives (e.g., classify_ai_system_risk) or state when not to use it, so it falls short of a 5.

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 state whether the tool is read-only, whether it makes external network calls (despite mentioning the SonnyLabs API), what side effects could occur, or any prerequisites like token expiration. The description says 'Checks' and 'Prevents' but provides no operational details beyond parameter explanations.

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 clear purpose, compliance rationale, and an Args/Returns breakdown. It is not overly verbose, and each section adds value. The compliance sentences are somewhat extra but contribute to purpose clarity, keeping it from being purely minimal.

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 description covers the core function, all parameters, and the return type, and an output schema is present. However, it lacks guidance on alternatives, prerequisite checks, or what to do with the returned analysis. For a compliance-related tool, more detail on how to interpret results would be helpful, making it minimally complete but not fully.

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 compensates for the 0% schema coverage by explaining each parameter in the Args block. For example, it clarifies file_path as 'the file path being accessed by the AI agent' and agent_action with examples ('read', 'write', 'execute'). This adds meaningful context beyond the bare schema, but could be more detailed about formats 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's function: 'Checks if AI agent is attempting to access sensitive files using SonnyLabs.ai API.' It identifies a specific verb (checks) and resource (sensitive file access), and this purpose is distinct from sibling tools that handle labeling, watermarking, or risk classification.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use the tool by referencing EU AI Act compliance and preventing unauthorized access. However, it does not explicitly mention when not to use it or name alternative tools, so it stops short of a 5.

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

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

With no annotations, the description carries the full burden of disclosing behavior. It explains that the tool classifies into four categories and returns obligations and deadlines, adding legal context. However, it does not disclose any limitations, error conditions, or side effects, which is a gap for a tool with no annotation support.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and concise: a clear purpose sentence, a list of output categories, a bulleted Args list, and a Returns line. Every sentence adds value, with no redundancy or fluff. The formatting is easy to scan.

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 description covers the classification framework, legal basis, and return value, and the presence of an output schema obviates the need for detailed return specifications. It is complete for its complexity, though it could mention how the boolean flags relate to the classification logic or cross-reference sibling tools for a fuller 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?

Despite 0% schema description coverage, the description provides a brief meaning for each of the 11 parameters (e.g., 'biometric_data: Uses biometric identification/categorization'). This compensates well for the schema's lack of descriptions, though some entries are terse and could benefit from more detail (e.g., 'interacts_with_users').

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: 'Determine AI system risk level per EU AI Act classification framework.' It specifies the output categories (PROHIBITED, HIGH-RISK, LIMITED-RISK, MINIMAL-RISK) and the legal basis (Articles 5, 6, and 50), distinguishing it from sibling tools that focus on specific aspects like prohibited practices or role determination.

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 classifying AI system risk under the EU AI Act, but it does not explicitly state when to use this tool versus alternatives like 'check_prohibited_practices' or 'determine_eu_ai_act_role'. No exclusions or alternative guidance is provided, so it relies on the user to infer the appropriate context.

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

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

No annotations are provided, so the description carries the full transparency burden. It discloses that the tool returns a role determination with definitions and obligations, implying a non-destructive analysis. However, it lacks details on validation, edge cases, or side effects, leaving room for ambiguity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the purpose and organized into a clear parameter list. While it is somewhat lengthy due to 12 parameters, each line is informative. The 'CRITICAL' emphasis is slightly extraneous but does not detract from overall structure.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of 12 parameters and existence of an output schema, the description covers all inputs and output expectations. It doesn't specify constraints like company_location format or whether at least one activity flag must be set, but the parameter explanations provide enough context for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage, but the description provides a one-sentence explanation for each of the 12 parameters, adding significant meaning beyond the bare titles. This fully compensates for the schema gap and gives clear semantics.

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 determines EU AI Act roles for an organization, which is a specific verb+resource. It distinguishes from sibling tools like risk classification or labeling, making the purpose unambiguous.

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 use when an organization needs to understand its obligations under the EU AI Act, and emphasizes role understanding as critical. However, it doesn't explicitly mention alternatives or when-not-to-use, but the context is clear.

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

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

With no annotations, the description carries the full burden. It discloses that the tool returns a dictionary with disclosure text and metadata, and gives an example. However, it does not elaborate on error behavior, required permissions, or any limitations, leaving some ambiguity typical for a simple getter.

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 clear opening, an example, and a compact args list. Every sentence adds value, and the example clarifies usage without verbosity.

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 the tool's simple nature, the description adequately covers purpose, parameters, and return format. It does not detail metadata fields, but the output schema exists and the example suffices for a low-complexity tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, but the description compensates by listing valid language codes (en, es, fr, de, it) and styles (simple, detailed, voice), plus defaults. This adds meaningful guidance beyond the raw schema properties.

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 function: 'Get AI interaction disclosure text for EU AI Act Article 50(1) compliance.' It specifies the resource (disclosure text) and the verb (get), and distinguishes it from siblings by focusing on AI interaction rather than deepfakes or watermarking.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use the tool: 'MUST be shown to users when they interact with an AI system.' It does not explicitly name alternative tools, but the compliance context and the distinct purpose among siblings imply when it applies.

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

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

No annotations are provided, so the description carries the full burden. It clearly implies a read-only operation through 'Get' and 'returns', and discloses the return structure ('Dictionary containing all available labels organized by content type') and the default language behavior. It does not mention side effects, but for a simple retrieval tool the non-mutating nature is evident.

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 one-sentence summary, a usage note, Args, Returns, and an Example. Every sentence serves a purpose, and the example clarifies invocation. No redundant or vague phrasing.

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 optional parameter, no required inputs) and the presence of an output schema, the description covers all necessary aspects: what it does, when to use it, the parameter format, and return shape. The example anchors the usage. No significant gaps remain.

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 only defines 'language' with a default and no description, so schema coverage is 0%. The description compensates by listing the allowed language codes ('en, es, fr, de') and the default, which is the full parameter semantics needed. It could add what language affects (e.g., localized label text), but the provided info is sufficient.

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 uses the specific verb 'Get' with a clear resource: 'all available deepfake and AI-generated content labels.' It also clarifies scope by naming content types (images, videos, audio, text), which distinguishes it from sibling labeling/watermarking tools that apply labels rather than retrieve template options.

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 explicitly states 'Use this to see what labels are available for images, videos, audio, and text,' giving clear context for when to call this tool. It does not name alternative tools, but the purpose is distinct enough from the sibling labeling tools that no explicit exclusion is necessary.

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 burden. It discloses that the text MUST be shown to users (compliance requirement) and that it returns a dictionary with disclosure text and metadata. This conveys the read-only, non-destructive nature and the expected output shape, though it doesn't mention edge cases or error 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 well-structured with Args, Returns, and an Example section. Every sentence adds value, and the format is easily parseable for an AI agent. 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?

This is a simple retrieval tool with two optional parameters, and the description provides complete context: purpose, parameter options, defaults, return type, and an example. No additional metadata is needed for correct 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?

Schema coverage is 0%, but the description fully compensates by documenting each parameter: language accepts en, es, fr, de, it; style accepts simple, detailed, privacy_notice. It also provides defaults and an example call, making the parameter semantics exceptionally clear 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?

The description uses a specific verb+resource combination ('Get emotion recognition disclosure text') and clearly scopes it to EU AI Act Article 50(3) compliance. It is distinct from sibling tools like get_ai_interaction_disclosure or get_deepfake_label_templates, which cover different disclosure types.

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 states when to use it (when an AI system uses emotion recognition technology and must show disclosure text). It does not explicitly name alternatives or exclusion criteria, but the purpose is clear enough that a link to siblings is not necessary.

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?

Given the lack of annotations, the description carries the full burden. It discloses what the tool returns (label text, placement guidance, compliance info), mentions formats (spoken/written), and notes the artistic work exemption. It does not describe side effects or limitations, but for a read-only label generation tool, this is adequate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and efficient. It fronts the core purpose, then details args, returns, and an example. Every sentence contributes value without redundancy, and the length is appropriate for the tool's complexity.

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 description covers all essential aspects: purpose, parameters, return format, placement guidance, and a concrete example. With an output schema present and no nested objects, this level of detail is complete. The mention of Article 50(4) and exemption adds regulatory context that a user needs.

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?

Despite 0% schema description coverage, the description fully explains each parameter in the Args section, including the language codes, the purpose of audio_description, and the exemption implication of is_artistic_work. The example further illustrates usage, completely compensating for the schema's lack of 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 opens with a specific verb and resource: 'Generate deepfake label for AI-generated or manipulated audio per Article 50(4).' This clearly states the tool's function and scope, distinguishing it from sibling tools like label_image_deepfake and label_video_deepfake.

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 context is explicitly tied to audio content and Article 50(4), making the intended use clear. However, it does not explicitly state when not to use it or name alternatives, though the focus on audio inherently differentiates it from the visual labeling 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?

With no annotations, the description carries the full burden. It discloses the output (label text, placement guidance, compliance info) and implies a non-destructive operation (generating guidance, not modifying the image). While it does not explicitly state side effects or limitations, the nature of the tool is clear and adequately 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 well-structured with a concise summary, a clear Args section, and an example. It is front-loaded with the main purpose and contains no unnecessary repetition or 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?

The description covers purpose, parameters, return values, and includes an example. It does not discuss error handling, edge cases, or interactions between the exemption flags, but for a labeling tool with a known output schema, this is reasonably 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 no descriptions (0% coverage), but the description's Args section thoroughly explains each parameter (image_description, is_artistic_work, is_satirical, language) and includes a usage example. This adds significant meaning beyond the schema and fully compensates for the schema's lack of 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's function: 'Generate deepfake label for AI-generated or manipulated images per Article 50(4).' This uses a specific verb (Generate) and resource (deepfake label for images), and distinguishes it from sibling tools like label_video_deepfake and label_audio_deepfake by explicitly focusing on images.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool: for labeling AI-generated or manipulated images. It does not explicitly exclude video/audio or mention alternatives, but the media-specific scope makes the intended usage obvious, especially when viewed next to sibling tool names.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the tool's purpose and returns a dictionary with labeled text and compliance info, but it does not disclose potential side effects, permission requirements, or whether input text is modified in place. This is adequate but not rich with additional 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?

The description is front-loaded with a one-sentence summary followed by an optional compliance context, an Args block, Returns, and Example. The structure is clear and each section adds value, though the compliance sentence slightly overlaps with the opening sentence. Overall, it is concise and well-organized.

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 text labeling tool with a relatively simple operation, the description covers purpose, inputs, output, and provides an example. The output schema exists (as noted), so the Returns section is an extra benefit. It lacks details on edge cases or the exact disclosure format, but it is sufficiently complete for most agentic use cases.

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 provides meaningful explanations for all four parameters, compensating for the 0% schema description coverage. It clarifies that text_content is the AI-generated text, has_human_editor is an exemption qualifier, editor_name is applicable only if an editor exists, and language includes a default and allowed values (en, es, fr, de). This exceeds the bare 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?

The description clearly states the tool's function: adding AI-generated content disclosure to news articles and public interest text. It references EU AI Act Article 50(4) compliance, which is a specific and distinct purpose compared to sibling tools like watermark_text or label_image_deepfake, making it easy to distinguish.

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 specifies the target content ('news, journalism, or public interest content'), providing clear contextual guidance for when to use this tool. However, it does not explicitly mention when not to use it or compare it to alternative siblings like watermark_text, so it stops short of full exclusions and alternative naming.

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?

There are no annotations, so the description carries the full burden. It explains the output (label text, placement guidance, compliance info) and notes that artistic/satirical works 'may qualify for exemption'. However, it does not disclose potential side effects, authentication requirements, or how exemption is determined, and it does not state that the tool only uses a description (not the actual video) beyond the arg description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is reasonably concise and front-loaded with purpose, followed by Args, Returns, and Example sections. Each section earns its place, though the Returns section is slightly redundant with the output schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a labeling tool with an output schema, the description covers purpose, all parameters, and return shape. It does not explicitly compare with sibling watermark tools or cover edge cases, but it provides sufficient context for an agent to decide when and how to call 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, but the description fully compensates by explaining each parameter's meaning, including the exemption implications for is_artistic_work and is_satirical, valid language codes, and the default. The example further clarifies expected usage.

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 uses a specific verb ('Generate') and clearly identifies the resource ('deepfake label') and scope ('AI-generated or manipulated videos per Article 50(4)'). It distinguishes the tool from siblings like label_image_deepfake and label_audio_deepfake by explicitly targeting videos.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use the tool: for labeling AI-generated/manipulated videos under Article 50(4). It also mentions exemption criteria for artistic/satirical works. It does not explicitly name sibling alternatives, but the video-specific wording implies the boundary.

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 carries the full burden. It discloses that the tool uses SonnyLabs.ai API and requires a Bearer token, indicating an external network call. It also describes the return type (a dictionary). However, it does not mention rate limits, data sent to a third party, error handling, or other side effects. This is moderately transparent but not richly detailed.

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 clear opening, context, an Args block, and a Returns line. It is not overly long, though one sentence ('Detects attempts to manipulate AI behavior through prompt injection') is somewhat redundant with the first line. Overall efficient 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?

The description covers all parameters and the return type, and it provides compliance context. An output schema exists, so the return format does not need full explanation. However, it lacks details on potential errors, rate limits, or the behavior of the external API. For a tool with external side effects, this is a small gap but not critical.

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 description compensates excellently by explaining each parameter: user_input is the text to scan, sonnylabs_api_token is a Bearer token, sonnylabs_analysis_id is the analysis ID, and tag is an optional identifier with a default. This adds meaningful context 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 it 'Scans user input for prompt injection attacks using SonnyLabs.ai API', providing a specific verb, resource, and scope. It distinguishes itself from siblings by focusing on prompt injection detection, while other tools handle different AI Act compliance 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 gives clear context: scanning user input for prompt injection and helping with EU AI Act Article 15 cybersecurity requirements. It implies when to use it (whenever prompt injection scanning is needed), but it does not explicitly mention alternatives or exclusions. Still, the context is strong enough for an agent to select this tool over 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?

With no annotations, the description carries the behavioral burden. It clarifies that the tool returns metadata and instructions and mentions fingerprinting/metadata embedding, suggesting no direct audio modification. However, it does not explicitly state whether file operations, permissions, or side effects are involved, leaving some ambiguity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured with a clear purpose statement followed by Args and Returns sections. The second sentence ('This tool provides metadata and instructions...') is slightly redundant with the first but adds context, so it retains 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?

For a relatively simple metadata-generation tool, the description covers the purpose, all parameters, and the output structure. The presence of an output schema offset the need for detailed return-value documentation, but usage guidance and examples are still missing, preventing a score of 5.

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?

Despite 0% schema coverage, the description explains each parameter in detail: audio_description as a brief description, generator as the AI system name, and format_type with valid values (mp3, wav, opus). This adds significant meaning beyond the schema's titles and defaults.

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 watermarking metadata for AI-generated audio per Article 50(2)' with a specific verb and resource. The 'audio' scope clearly distinguishes this from sibling tools like watermark_text, watermark_image, and watermark_video.

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?

Usage context is implied through the tool name and reference to AI-generated audio, but the description does not explicitly state when to prefer this over sibling watermarking tools or mention any exclusions. No alternatives are given, so the guidance remains 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 provided, the description carries the full burden. It explicitly says the tool 'generates watermarking metadata and instructions' rather than actually modifying the image, which is a key behavioral trait. It also discloses the return type (dictionary with metadata, instructions, compliance info) and mentions the watermark must be machine-readable and detectable. This provides useful transparency without contradicting any annotations.

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 organized into short sections (Args, Returns, Example) with the core purpose front-loaded in the first sentence. It is slightly longer than strictly necessary due to the example, but every sentence contributes useful information, making it efficient rather than verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with three params, a simple dictionary output, and no annotations, the description is highly complete. It includes the regulatory reference (Article 50(2)), the output shape, parameter semantics, and a worked example. The presence of an output schema means return values need not be detailed further, and the description provides enough context for an agent to select and invoke the tool correctly.

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 does: each parameter is explained clearly (image_description, generator, format_type), including example values for generator ('DALL-E', 'Midjourney') and format_type ('png', 'jpg', 'webp') with a default. The example call demonstrates usage. This adds real meaning beyond the schema's bare property names and defaults.

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 first sentence 'Generate watermarking metadata for AI-generated images per Article 50(2)' clearly states a specific verb ('generate') and resource ('watermarking metadata for AI-generated images'), distinguishing it from sibling tools like watermark_text, watermark_video, and watermark_audio by explicitly scoping to images. The mention of C2PA compliance adds further specificity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly indicates this tool is for AI-generated images, providing context that it produces C2PA-compliant metadata and instructions. However, it does not explicitly mention when not to use this tool or point to alternatives (e.g., watermark_text for text), though sibling names make the distinction implicit. This fits the 'clear context, no exclusions' level.

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. It discloses that the tool 'adds machine-readable metadata' and returns 'a dictionary containing the watermarked text with embedded metadata.' This gives a clear behavioral model, though it doesn't mention side effects like possible text modifications or reversibility.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with a one-sentence summary, followed by a clear breakdown of arguments, return value, and an example. Every sentence provides value without fluff, and the structure is well organized.

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 description covers the legal/regulatory context (EU AI Act), parameter semantics, return type, and gives a concrete example. Given the tool's simplicity (3 parameters, flat structure) and the presence of an output schema, this is fully sufficient for an agent to select and invoke it correctly.

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 explains every parameter (text_content, generator, format_type) with explicit meanings, defaults, examples (e.g., 'GPT-4' for generator, 'plain, markdown, html' for format_type), and even includes a usage example.

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 ('Add metadata watermark'), the target resource ('AI-generated text'), and the specific compliance purpose ('EU AI Act Article 50(2)'). This distinguishes it from sibling tools like watermark_image, watermark_video, and watermark_audio, which handle other modalities.

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 strong context by stating this is 'required for provider compliance with Article 50(2) for text content generation systems.' It implies usage for text content but does not explicitly name alternatives or exclusion criteria, so it falls short of a 5.

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. It clarifies that the tool generates metadata and instructions rather than directly embedding watermarks, and it discloses the output shape and compliance standard. It could add a note about non-modification, but the current wording is clear enough.

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, front-loaded with purpose, and uses a clear Args/Returns structure. Every sentence adds relevant information without 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?

The tool is low-complexity with only three parameters, and the description covers purpose, parameters, compliance, and return value. An output schema exists and is referenced via the Returns statement. It would benefit from an explicit alternative-tool mention, but it is otherwise 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 description coverage is 0%, so the description must compensate. The Args section gives meaningful definitions for all three parameters, including valid format examples (mp4, webm, mov) and the meaning of 'generator'. This adds value beyond the bare schema fields.

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 uses a specific verb ('Generate watermarking metadata') and resource ('AI-generated videos per Article 50(2)'), clearly distinguishing it from sibling watermark tools that target images, audio, or text. It also adds C2PA compliance context, making the tool's purpose unambiguous.

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 clearly states the tool is for AI-generated videos with C2PA-compliant metadata, giving a clear when-to-use context. It does not explicitly name alternative tools for images/audio/text, but the video-specific scope makes the intended use evident.

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