mcp-eu-ai-act

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 the outcome (proof_id, verification URL) and a prerequisite (certified plan), but does not explicitly state whether this is a mutation, any side effects, or error conditions. The term 'tamper-proof' implies immutability, but the lack of explicit disclosure about the operation's nature or limitations leaves gaps.

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

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

Two sentences, both informative and free of filler. The first sentence front-loads the purpose and mentions key inputs/outputs; the second handles the prerequisite. Every word earns its place, making it concise and well-structured.

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 only 2 parameters and no output schema, the description is fairly complete. It covers the action, the inputs, the outputs, a prerequisite, and the audit context. It does not explain potential failure modes or operational details like rate limits, but for a simple certification tool, it provides enough context for an agent to 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 100%, so baseline is 3. The description adds context for 'report_data' by saying 'pass the report JSON,' which helps clarify that the parameter takes the JSON string. However, it does not add much meaning for 'trust_layer_key' beyond the schema's 'ArkForge Trust Layer API key.' The description does link the parameters to the overall workflow, providing marginal added 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's function with a specific verb ('Make your compliance report tamper-proof') and resource ('compliance report'). It distinguishes itself from siblings by specifying the output (proof_id and public verification URL) and the prerequisite of running generate_report() first, which sets it apart as a certification step rather than a generation or checking tool.

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

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

Provides clear usage context: 'Run generate_report() first to produce the report' establishes a prerequisite and sequence. It also notes 'Certified plan required,' indicating a necessary condition. It does not explicitly name alternatives, but the guidance to run generate_report() first implies this tool is for post-processing, and the sibling list shows alternatives like generate_report, so 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 provided, the description carries the disclosure burden. It explains behavior: no required arguments (optional params with defaults), a 5-second runtime, output includes which articles are violated, and step-by-step fix instructions. This goes beyond the schema and provides useful operational context, though it does not address side effects or authentication.

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

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

Three concise sentences front-load the core purpose and quickly convey key details: no required arguments, output specifics, and a sibling alternative. The article list is specific without being excessive. Every sentence contributes value with 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?

The description covers what the tool does, what it returns (violations and fixes), performance, and how to extend to GDPR. Given only two optional params and no output schema, this is fairly complete. However, it omits any mention of authentication or project state implications, which could be relevant for a compliance-check 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 input schema already provides 100% coverage with detailed descriptions for both parameters (project_path and risk_category), including defaults and semantics. The description's 'no arguments' is consistent with the defaulted optional parameters but adds no new meaning beyond the schema. Baseline of 3 is appropriate.

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 EU AI Act compliance, specifies the verb 'check' and resource 'EU AI Act requirement', and lists specific articles covered (Art. 52, 11, 14, 15). It distinguishes itself from siblings by explicitly naming combined_compliance_report for the dual EU+GDPR scenario, making its scope clear.

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 a clear usage context: use this tool for EU AI Act-only compliance checks. It explicitly points to combined_compliance_report() when both EU AI Act and GDPR are needed, serving as an alternative. It does not mention exclusions or when to use other sibling tools, but the given guidance 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?

No annotations are provided, so the description must disclose behavior. It does so effectively by mentioning 'no setup, under 10 seconds, free,' and detailing the output: 'returns pass/fail per article with a prioritized fix list.' However, it does not explicitly state limitations, error conditions, or side effects, and it omits a direct mention of being read-only (though 'scan' implies it).

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 four sentences, front-loaded with a clear call to action ('Run this before your next deploy'). It packs in key details like speed and cost. The sentence about fines is somewhat promotional but still relevant for motivating compliance; overall, it is concise without 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?

For a tool with no output schema and no annotations, the description is remarkably complete. It covers purpose, when to use, what it detects, output format, performance, and cost, and it distinguishes from siblings. No critical missing context that would prevent 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 description coverage is 100%, so the baseline is 3. The description's phrase 'no arguments' aligns with the schema's optional parameters but does not add extra meaning or clarify parameter usage beyond what the schema already provides.

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 the tool's purpose: 'reveals every EU AI Act + GDPR gap in your codebase' with a clear verb ('reveals') and resource ('gaps'). It also distinguishes itself from siblings by saying it 'Replaces separate scan_project() and gdpr_scan_project() calls,' making its unique value transparent.

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 a concrete usage directive: 'Run this before your next deploy.' It also provides an explicit alternative exclusion: 'Replaces separate scan_project() and gdpr_scan_project() calls,' clearly telling the agent when to choose this tool over the 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?

With no annotations, the description carries the burden. It discloses the output (pass/fail for each article with fix instructions) and that it does not itself fix gaps. However, it doesn't mention whether the tool scans files, requires authentication, or any side effects. The 'no arguments' phrase contradicts the actual schema parameters, which is a transparency gap.

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

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

The description is reasonably concise and front-loaded with the main purpose. The sentence about GDPR fines adds motivational context but is not essential to tool behavior. The inaccurate 'no arguments' phrase is unnecessary and reduces structural quality.

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 what it checks (specific articles), what it outputs (pass/fail with fix instructions), and provides an alternative for combined compliance. It lacks details on prerequisites or how the project path is used, but the schema covers parameters. The overall context is sufficiently complete for a check 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 input schema has 100% description coverage for both parameters, so baseline is 3. However, the description falsely states 'no arguments', which contradicts the schema and could mislead an agent into thinking no parameters exist. This misinformation warrants a below-baseline score.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that the tool checks whether a project passes or fails GDPR requirements, lists the specific articles covered, and indicates the output (pass/fail with fix instructions). It also distinguishes itself from combined_compliance_report for EU AI Act + 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?

The description explicitly directs users to combined_compliance_report() when they need EU AI Act + GDPR together, implying this tool is for GDPR-only checks. However, it doesn't explicitly state when NOT to use this tool, and the phrase 'no arguments' is slightly misleading given the schema.

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 misleadingly says 'no arguments' despite the schema defining two optional parameters. It also discloses no behavioral traits such as whether it writes a file, returns a string, requires permissions, or has side effects. The claim about combining data, gap analysis, and remediation steps is content-focused, not behavior-focused.

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 front-loaded with the core action, and the three sentences are well-organized. However, the inaccurate 'no arguments' phrase wastes a few words and detracts from precision, preventing a perfect score.

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 no output schema, so the description needs to explain return values. It mentions a 'structured document' but not its format (e.g., PDF, JSON) or delivery method (file, response). It does cover the main purpose and audience, but missing return-type and side-effect details leave notable gaps.

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 100%, so the baseline is 3, but the description's 'no arguments' claim contradicts the schema, undermining clarity. The description adds no meaningful parameter details beyond what the schema already provides, and actively misleads about parameter existence.

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 'Generate the GDPR report' with a specific resource and verb, and distinguishes from the generic 'generate_report' sibling by adding GDPR-specific context (DPO, audit prep, regulatory response). It also explicitly names the alternative 'combined_compliance_report' for combined EU AI Act + GDPR, so it stands out from siblings.

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

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

The description gives explicit usage context (DPO review, audit prep, regulatory response) and names a concrete alternative for a related but different scenario ('For EU AI Act + GDPR together, call combined_compliance_report()'). This clearly helps an agent decide when to use this tool versus alternatives.

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 templates are pre-filled and tailored to processing role, and that users must manually fill in [bracketed] sections. It also suggests a precursor action. However, it does not specify the return format or whether any files are created, which is a minor gap for a tool without output schema or 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 three sentences, front-loaded with the action ('Get pre-filled GDPR templates'), lists specific deliverable types, and includes a usage tip. Every sentence contributes value without 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?

Despite no output schema, the description conveys that the tool returns template content with placeholders, lists what types of templates, and provides a prerequisite step. The only gap is ambiguity about whether output is a single combined document or separate documents, but this is acceptable for a simple 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 input schema fully describes the single 'processing_role' parameter with enum values and a default, so schema coverage is 100%. The description adds little beyond restating ('tailored to your processing role') and the 'no arguments' phrase could confuse optionality. Baseline of 3 is appropriate.

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 pre-filled GDPR templates and lists specific document types (Privacy Policy, DPIA, ROPA, Data Breach Procedure). However, it does not explicitly differentiate from the sibling 'generate_compliance_templates', and the phrase 'no arguments' is slightly misleading given the optional 'processing_role' parameter.

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 a concrete usage prerequisite: 'Run gdpr_check_compliance() first to see which documents you're missing.' This gives clear when-to-use context, but it does not name alternatives or exclusion criteria for when not to use this 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. It discloses performance ('5 seconds'), cost ('free'), and output ('Returns flagged files with data categories and applicable GDPR articles'). However, it does not explicitly state whether the tool is read-only, requires authentication, or has side effects (e.g., modifying files, sending data). The term 'no arguments' is also behaviorally misleading given the schema's optional project_path parameter.

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 compact and front-loaded with the main purpose. The sentence about GDPR fines ('GDPR fines reach 20M EUR or 4% turnover') is promotional and doesn't explain tool behavior, making it somewhat wasteful. The first two sentences are efficient, but the fines sentence could be removed for better 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?

For a tool with one optional parameter and no output schema, the description covers the key aspects: what it scans, what it returns, and an alternative. However, it omits practical context such as whether authentication is needed, whether the scan is truly project-local, and any limitations (e.g., files that cannot be scanned). The mention of being 'free' suggests a possible registration or API key requirement not clarified here.

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 description coverage is 100% for the single project_path parameter, so the schema already explains the argument well. The description's claim 'no arguments' directly contradicts the schema, which shows an optional parameter with a default. This actively confuses the agent instead of adding 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's core function: 'Find every file in your project that touches personal data.' It goes beyond a simple verb+resource by specifying what it detects (PII fields, cookies, tracking pixels, analytics SDKs, consent flows) and what it returns (flagged files, data categories, GDPR articles). This distinguishes it from siblings like combined_compliance_report and scan_project.

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 names an alternative: 'For EU AI Act + GDPR together, call combined_compliance_report().' This signals when to use this tool (GDPR-only scanning) versus a more comprehensive option. However, it doesn't provide full when-not-to-use guidance for other similar siblings like gdpr_check_compliance or scan_project, leaving some ambiguity.

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

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

No annotations are provided, so the description carries the burden. It discloses auto-population from project scan, SHA-256 hash, and plan requirement, but it misleadingly says 'no arguments' while the schema has optional parameters, and omits details about optional trust layer signing or 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?

The description is compact and front-loaded, with each sentence contributing key info (purpose, auto-population, hash, timeline, plan requirement). However, the inaccurate 'no arguments' claim slightly tarnishes the 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?

For a tool with no output schema and no annotations, it provides useful outcome hints (8 sections, SHA-256 hash) but does not specify the return format or mention the optional trust layer signing behavior. This leaves some gaps in what the agent can expect.

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 100% with rich descriptions, so a baseline of 3 would apply, but the description actively misleads by claiming 'no arguments', contradicting the schema's optional parameters. This reduces clarity rather than adding 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 it builds an Annex IV evidence package for high-risk AI, using the specific verb 'Build' and a distinct resource. This differentiates it from generic report tools like generate_report and check_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?

It provides clear context: intended for auditor needs, high-risk AI, applies Aug 2026, and Pro plan required. However, it does not explicitly state when not to use it or name alternative tools, so it lacks explicit 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?

With no annotations, the description carries the burden and does well: it discloses auto-scanning behavior, prioritization logic, quick-win sequencing, deadline feasibility assessment, and the Pro plan requirement. It doesn't mention side effects or output format, but for a plan-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 two sentences, front-loaded with the main value proposition, and includes essential context (features, Pro plan, alternative) without 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?

Without an output schema, the description provides a good overview of what the plan delivers (week-by-week, prioritized, quick wins, feasibility). It doesn't explicitly state the output format, but the core features and differentiation from sibling tools are covered sufficiently.

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 covers all three optional parameters with clear descriptions, achieving 100% schema coverage. The description adds the default deadline mention but also says 'no arguments,' which could be misleading since there are optional parameters. Baseline 3 is appropriate as the schema does the heavy lifting.

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: generating a prioritized, week-by-week compliance roadmap. It specifies key features (auto-scans project, ranks fixes, sequences quick wins, tells if deadline is achievable) and distinguishes from check_compliance by mentioning the free alternative.

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 notes that a Pro plan is required and suggests check_compliance() for a free gap summary, providing an explicit alternative. It implies use when you need a full plan rather than a summary, though it doesn't enumerate all alternatives 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?

With no annotations provided, the description carries the full burden. It discloses key behaviors: saves to docs/, requires filling in [bracketed] sections, and produces all 6 mandatory documents for high-risk systems. It does not mention side effects like overwriting files or authorization needs, but the workflow and output location are clearly described, which is more than many tools provide.

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 compact (3 sentences) and every sentence adds value: the purpose, the high-risk detail, output location, required follow-up, and a prerequisite step. There is no fluff or repetition of the schema.

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

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

Given the tool's simplicity (one parameter, no output schema) and the lack of annotations, the description is fairly complete. It covers what the tool does, what it generates per risk category, where files are saved, and what to do next. It does not detail the exact count for non-high risk categories, but the 'tailored to your risk category' wording covers that implicitly.

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 already describes risk_category with a clear enum and note that templates are most useful for high risk. The description adds meaning by explaining that templates are tailored to the risk category and that high-risk systems get all 6 mandatory documents, which gives context on how the parameter affects the output.

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 pre-filled compliance templates for specific document types (risk management, technical documentation, transparency notice, human oversight policy) and tailors them to the risk category. It distinguishes itself from sibling tools like check_compliance by positioning itself as the generation step, though it does not explicitly name alternatives in the same way as the high calibration example.

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?

It provides a clear usage context: use when you need compliance docs instead of writing from scratch. It also gives an explicit prerequisite step ('Run check_compliance() first to see which documents you're missing'), which is scheduling guidance. However, it does not explicitly state when to use this tool over alternatives like gdpr_generate_templates, 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 takes on full transparency duty. It discloses key behaviors: auto-detection of AI frameworks, running gap analysis, and generating a structured remediation plan. The 'no arguments' claim is slightly misleading because the schema has two optional parameters, but it likely means no required arguments, which is accurate. Overall, it provides useful behavioral context beyond the bare schema.

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 compact and front-loaded with the tool's purpose, followed by key behavioral highlights and a practical note about no API key. Every sentence earns its place, and there is no fluff or repetition of schema 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 tool's moderate complexity, the description adequately explains what the tool does, how it works (auto-detection, gap analysis), and what it produces (remediation plan for legal review/DPIA). It does not explain return format, but no output schema exists. The absence of any reference to sibling tools or limitations prevents a 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?

The schema covers 100% of parameter descriptions, so the baseline is 3. The description adds some context by implying that no arguments are needed and that the tool auto-detects, which clarifies the optionality of project_path. However, it does not add further meaning to risk_category beyond the schema's enum description, so no extra credit is warranted.

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 compliance report with specific outputs (gap analysis, remediation plan) and identifies the target audience (legal team). It does not explicitly distinguish it from sibling tools like combined_compliance_report or gdpr_generate_report, but the verb+resource is specific enough to convey the core 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?

The description gives clear context for when to use the tool ('when your legal team asks for a compliance report') and notes that it requires no API key, implying low friction. However, it does not mention when not to use it or suggest alternatives, 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, the description carries the full burden for behavioral disclosure. It states 'No arguments' and explains that it shows pricing, which implies a read-only operation, but it does not explicitly confirm that it is safe or free of side effects, nor does it clarify authentication requirements. The cross-reference to register_free_key adds context but doesn't fully clarify the tool's own 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, front-loading the core purpose, then providing specific plan details, then the no-arguments note, and finally a helpful pointer. Every sentence earns its place without redundancy or unnecessary length.

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 no-argument tool with no output schema, the description is fully complete. It explains what the tool does, the key plans, and the activation step. Given the low complexity and empty schema, nothing important is missing.

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

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

The tool has zero parameters and the schema coverage is 100%, so the description's explicit statement 'No arguments' fully aligns with the schema. The baseline for 0-param tools is 4, and the description adds clarity by reinforcing the no-argument requirement.

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: 'See pricing and features for every plan' with specific plan details. This distinguishes it from all sibling tools, which focus on compliance and scanning rather than pricing. The verb 'See' and resource 'pricing and features' are specific and 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 provides clear context: use this tool to view pricing and plan details. It also gives a follow-up action ('Call register_free_key() to activate your free API key'), which is a useful when-to-use-next pointer. However, it doesn't explicitly state when NOT to use this tool or how it compares to alternatives, but no pricing alternative exists 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?

With no annotations, the description effectively discloses key behavioral traits: it activates a free key, requires only an email (no password/credit card), and imposes a strict requirement to use the exact user-provided email. It does not detail side effects like account creation or email confirmation, but for the tool's simplicity, it is well-covered.

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

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

The description is two sentences with an imperative warning, all directly relevant and free of fluff. It front-loads the purpose and then delivers the critical usage instruction, with 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?

For a single-parameter tool with no output schema, the description is complete: it states the action, the input condition, and the procedural requirement. The sibling context (e.g., validate_api_key) helps distinguish use cases, and no further information is necessary 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 already documents the email parameter fully with a 'MUST come from the user's message' note. The description adds operational context—'wait for their reply' and 'Do NOT pass a placeholder or fabricated email'—reinforcing and expanding on the schema, which adds value beyond the schema alone.

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 'Activate a free API key — unlocks scan history and CI/CD integration,' using a specific verb and resource while distinguishing this from sibling tools like validate_api_key. The purpose is immediately clear and 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 provides strong procedural guidance: 'ask the user to type their email first, wait for their reply, then call this with the exact email they typed.' It does not explicitly name alternatives or excluded cases, but the context makes it clear this is for new free-key registration, slightly missing a direct when-not/alternative comparison.

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 must carry the full burden. It claims 'no arguments, no setup' but the input schema defines two optional parameters, making the description misleading. It does add some context (5-second scan, returns risk category and legal actions), but omits important behavioral details like file system access, output structure, or error conditions.

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 three sentences long and front-loaded with the core value proposition. However, the 'Enforcement live since Feb 2025 — fines up to 35M EUR' sentence is marketing fluff that does not help an agent select or invoke the tool, costing it a perfect score.

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 explains what the tool does and what it returns (risk category and legal actions), which is sufficient for a simple, optional-parameter tool. However, it contradicts the existence of parameters, fails to describe return format (no output schema exists), and does not explain the 'follow_imports' option, leaving meaningful gaps.

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 100%, so the baseline is 3, but the description actively contradicts the schema by saying 'no arguments' while two parameters exist. It adds no useful parameter semantics beyond the schema and actively misleads.

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 action: scanning a project to determine if it triggers EU AI Act obligations. It names specific frameworks scanned and the output (risk category and legal actions), and distinguishes itself from the sibling 'combined_compliance_report' by noting the alternative for EU AI Act + GDPR together.

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 explicit usage context: use it 'before you ship' and shows when to choose an alternative ('For EU AI Act + GDPR together, call combined_compliance_report() instead'). This gives clear when-to-use and when-not-to-use guidance.

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

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

No annotations are provided, so the description carries the full burden. It discloses that the tool performs a classification ('Returns matched category...with confidence level and triggering risk indicators'), and that it does not require a scan or API key, indicating a read-only, stateless operation. It does not explicitly state limitations (e.g., advisory only), but for this simple input-output tool, the description is 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 two sentences, front-loaded with the action and result. The first sentence tells the user what to do and what to expect; the second details the output fields. Every sentence earns its place, with no redundant or filler 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?

Given the tool's simplicity (one string parameter, no output schema), the description is complete. It states the input, the output (category, confidence, indicators), and the key prerequisites (no scan, no API key). There is no missing information that would prevent an agent from using 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?

The schema describes the single parameter 'system_description' with examples and clearly explains what to input. Schema coverage is 100%, so the tool description does not need to add much. The description reinforces the parameter by saying 'Describe what your AI system does in plain language,' but it does not add new semantic information 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's function: 'Describe what your AI system does in plain language — get back your EU AI Act risk tier, the articles that apply, and your first compliance step.' It specifies the resource (EU AI Act risk tier) and the verb ('get back'). It also distinguishes this tool from siblings by noting 'No project scan needed, no API key,' setting it apart from scan and report 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?

The description provides clear usage context: it works directly from a text description without requiring a project scan or API key. This implies it is for quick, standalone assessments, contrasting with tools like scan_project. While it does not explicitly name alternatives or say 'use this instead of X', the 'no project scan' note conveys when this tool is 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 are provided, so the description must convey behavioral traits. 'Check' implies a read-only operation, which is helpful, but the description does not disclose error behavior for invalid keys, authentication requirements, or any potential side effects. For a simple validation tool, this is adequate but 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?

A single sentence with a front-loaded action and an em-dash enumerating return values. No wasted words; every part of the description 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?

For a single-parameter tool with no annotations or output schema, the description covers the purpose and key return fields sufficiently. It lacks discussion of error cases or prerequisites (e.g., needing a registered key), but the tool's simplicity mitigates this gap.

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

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

The schema already has 100% coverage with 'The API key to validate.' The tool description adds context that it's 'your' key and links it to returned usage stats, but this provides minimal extra meaning beyond the structured schema. Baseline 3 is appropriate.

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 ('Check') and clearly identifies the resource ('your API key status'). It enumerates the return values (plan tier, email, usage stats), which unambiguously distinguishes it from sibling tools like register_free_key or scan_project.

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 'Check your API key status' implies when to use it: when you need to validate your own key and see account/usage details. No exclusions or alternative tools are cited, but the simplicity of the tool makes the usage clear. A more explicit 'use this instead of get_pricing' would elevate it, but it's not necessary.

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