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 burden of behavioral disclosure. It explains the transformation (tamper-proofing), the output (proof_id, URL), and prerequisites (generate_report, certified plan). It does not cover rate limits or side effects, but for a certification action this is reasonable and informative.

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 no redundant words. The main purpose and key outputs are front-loaded, and the prerequisites are in the second sentence. Every piece contributes to understanding.

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 the return values (proof_id, verification URL) and the audit context (Art. 12, auditors), which compensates for the absence of an output schema. It also gives a prerequisite and a required plan. Minor gaps like error handling or key input are not significant given the simple parameter set.

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 describes both parameters fully (report_data as JSON string, trust_layer_key as API key). The description reinforces 'pass the report JSON' but adds no new parameter semantics beyond what the schema provides, so the baseline score 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 starts with a specific verb phrase 'Make your compliance report tamper-proof' and identifies the resource (compliance report) and the outcome (proof_id and public verification URL). It clearly distinguishes this certification tool from sibling 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 a clear prerequisite: 'Run generate_report() first to produce the report' and notes 'Certified plan required.' This gives strong guidance on when to use the tool, though it does not explicitly state when not to use it or name alternative tools.

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

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

No annotations are provided, so the description carries the full burden. It discloses speed (~5 seconds), that it returns pass/fail per requirement, and that it provides fix instructions. However, it incorrectly claims 'no arguments' while the schema defines two optional parameters, and it does not state whether the operation is read-only (though 'check' 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 compact (three sentences) and front-loaded with the main purpose. The phrase 'no arguments' is inaccurate and wasteful, but otherwise every sentence adds useful context such as specific articles and the alternative combined tool.

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 results look like (pass/fail, article-level violations, step-by-step fixes), so return values are covered despite no output schema. It does not mention risk_category semantics, but the schema provides that. The tool is sufficiently described for an optional-parameter 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?

Schema description coverage is 100%, so the baseline is 3. The description does not add param semantics, and its claim of 'no arguments' is misleading given the optional project_path and risk_category parameters. Schema descriptions already cover both parameters, so the description neither helps nor hurts beyond the baseline.

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, lists specific articles (Art. 52, 11, 14, 15), and mentions fix instructions. It also distinguishes itself from combined_compliance_report for GDPR+AI Act.

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 explicitly directs users to call combined_compliance_report() for combined EU AI Act + GDPR, implying this tool is for EU AI Act only. It does not mention when to prefer this over gdpr_check_compliance, but the name and the mention of EU AI Act requirements provide sufficient 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?

With no annotations, the description carries the full burden of disclosing behavior. It states 'no arguments, no setup, under 10 seconds, free,' describes detection scope ('AI frameworks and personal data flows'), and explains the return format ('pass/fail per article with a prioritized fix list'). This is solid behavioral context, though it does not explicitly state that the operation is read-only or non-destructive, which is implied by 'detects.'

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 strong call to action and efficiently packs key facts: scope, speed, cost, detection targets, output structure, and sibling replacement. The sentence about fines adds persuasive context but is not strictly necessary for tool invocation; however, it reinforces urgency and value. Overall, it's tight and well-ordered.

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 there is no output schema, the description compensates by explicitly describing the return value: 'pass/fail per article with a prioritized fix list.' It covers the tool's scope, performance, and positioning relative to alternatives. It does not describe edge cases or how the optional parameters affect results, but the schema already documents those parameters, so the description is sufficiently complete for a scan tool.

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

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

Schema description coverage is 100%, and each parameter is fully explained in the schema (e.g., 'Omit entirely or pass '.' to scan the current working directory'). The description adds no new parameter information beyond saying 'no arguments,' which is consistent with all parameters being optional with defaults. Baseline 3 is appropriate because the schema handles parameter 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?

The description clearly states a specific action: 'Run this before your next deploy' and 'One call reveals every EU AI Act + GDPR gap in your codebase.' It explicitly distinguishes the tool from siblings by saying 'Replaces separate scan_project() and gdpr_scan_project() calls,' making it unambiguous what this tool does and how it differs.

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 clear when-to-use instruction ('Run this before your next deploy') and explicitly names the alternatives it replaces ('scan_project() and gdpr_scan_project()'). This provides concrete guidance for selecting 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 provided, the description carries the full burden of behavioral disclosure. It reveals the output format (pass/fail per article, fix instructions) but fails to mention that the tool takes arguments (or that they are optional), and does not explain how the compliance check is performed (e.g., static analysis, scanning files). The 'no arguments' statement is actively misleading about the tool's behavior.

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

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

The description is reasonably concise (four sentences) and front-loads the primary purpose. However, the sentence about GDPR fines ('GDPR fines reach 20M EUR or 4% turnover') is promotional and not directly relevant to tool invocation, adding unnecessary noise. The structure could be tightened by removing that filler and correcting the parameter claim.

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 and no annotations, so the description needs to provide complete context. It does well by outlining the covered articles and the pass/fail output with fix instructions. But it fails to mention the two optional parameters, misrepresents argument usage, and lacks any explanation of the underlying mechanism (e.g., does it scan the project path?). This leaves significant gaps for an agent deciding 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?

Schema coverage is 100% (both project_path and processing_role have descriptions), so the baseline would be 3. However, the description's assertion 'no arguments' directly contradicts the schema, adding no value and potentially confusing an agent. The parameter descriptions in the schema are self-explanatory, but the false claim in the description outweighs the baseline benefit.

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 whether your project passes or fails each GDPR requirement' and lists specific articles (Art. 6, 7, 15–22, 32, 33–34), giving a focused scope. It also distinguishes itself from combined_compliance_report by directing users there for EU AI Act + GDPR. However, the claim 'no arguments' is inaccurate given the input schema, which slightly undermines clarity.

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

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

The description explicitly names an alternative: 'For EU AI Act + GDPR together, call combined_compliance_report().' This provides a clear when-to-use vs. alternative. However, the misleading statement 'no arguments' contradicts the actual schema and could cause an agent to ignore the optional project_path and processing_role parameters, which is a significant usage guidance flaw.

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

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

No annotations are provided, so the description bears the full burden. It states 'no arguments' but the schema defines two optional parameters, which is misleading. It also doesn't disclose whether the tool performs read-only scanning, requires permissions, or has side effects. The intended output content is described, but behavioral transparency is undermined by the inaccurate 'no arguments' claim.

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

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

Two concise sentences, front-loaded with the core purpose. The first sentence states the tool's function and output; the second covers the alternative. No filler or 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 there's no output schema, the description explains what the report contains and its use cases, which is sufficient for basic understanding. It doesn't clarify how the report is generated or the return format, but considering the tool's simplicity and sibling alternatives, it's 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?

Schema coverage is 100% and the parameter descriptions are clear (project_path and processing_role with their defaults). The description itself adds nothing beyond the schema, and the 'no arguments' phrase conflicts with the existence of parameters, but the schema sufficiently documents the parameters.

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

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

The description clearly states the tool generates a GDPR report, listing specific contents (personal data inventory, gap analysis, remediation steps). It also explicitly differentiates from combined_compliance_report, 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?

Provides strong context (DPO review, audit prep, regulatory response) and explicitly names combined_compliance_report for EU AI Act + GDPR cases. However, it doesn't distinguish when to use this over other GDPR siblings like gdpr_check_compliance or gdpr_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?

The description falsely claims 'no arguments' while the schema includes an optional processing_role parameter. This misleads the agent about the tool's configurability. No annotations exist to carry safety or side-effect information, so the description must disclose behavior; it fails to mention that the processing role affects output or that the default is 'controller'. The 'no arguments' statement actively contradicts the schema, reducing transparency significantly.

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

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

The description is relatively concise at four sentences, front-loading the core function and list of templates. The flow moves from what you get to usage ('Fill in [bracketed] sections') to prerequisite. However, the erroneous 'no arguments' phrase wastes space and could be removed, preventing a top 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?

Given no output schema and no annotations, the description should clarify what the tool returns (e.g., text, downloadable files) and how the processing role affects the templates. It neither mentions the output format nor explains the role's effect. The false 'no arguments' claim adds confusion. The prerequisite reference is helpful but does not make up for missing critical details about return type and role-driven customization.

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?

Although the schema description covers 100% of the parameter, the tool description says 'no arguments', directly contradicting the existence of processing_role. The description does not add any value to the parameter meaning; instead it actively misleads. This lowers the effective parameter semantics below the baseline for high schema coverage because the misinformation outweighs the schema's clarity.

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 retrieves pre-filled GDPR templates, listing specific document types (Privacy Policy, DPIA, ROPA, Data Breach Procedure). It also distinguishes itself from siblings like gdpr_generate_report and check_compliance by focusing on template generation. The verb 'Get' plus resource and scope makes the purpose highly 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?

Provides explicit when-to-use guidance by instructing to 'Run gdpr_check_compliance() first to see which documents you're missing.' This establishes a clear workflow and prerequisite, helping an agent decide when to invoke this tool versus alternatives. It also implies the tool is useful when missing specific documents, which is strong usage direction.

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 discloses return value composition ('flagged files with data categories and applicable GDPR articles') and implies a read-only scan ('Find every file'). It also mentions '5 seconds' and 'free' as operational traits. The 'no arguments' claim is slightly offset by the optional project_path parameter, but not harmful.

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 utility sentence 'GDPR fines reach 20M EUR or 4% turnover' is irrelevant to selecting or invoking the tool and wastes a slot. The useful information (function, speed, cost, output) is front-loaded, but the fine scare line is extraneous.

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 simple (one optional parameter, no output schema) and the description sufficiently explains what it does, what it scans for, and what it returns. It also provides a pointer to an alternative for combined requirements, making it complete for practical use.

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

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

Schema coverage is 100%, with a single optional parameter already well-described ('Leave empty or pass '.' to scan the current directory'). The tool description adds no parameter-specific meaning beyond saying 'no arguments', which actually understates the optional project_path parameter. Thus baseline 3 applies.

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 a specific verb and resource: 'Find every file in your project that touches personal data.' It enumerates what is detected (PII, cookies, tracking pixels, analytics SDKs, consent flows) and distinguishes itself from generic scan_project by focusing on GDPR. It also points to combined_compliance_report for a different scope.

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 context on when to use this tool ('no arguments, 5 seconds, free') and explicitly directs users to combine with EU AI Act via combined_compliance_report(). However, it does not articulate exclusions or contrast with sibling tools like gdpr_check_compliance or scan_project.

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

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

With no annotations, the description must carry the burden of behavioral disclosure. It does mention the SHA-256 hash, Pro plan requirement, and auto-population, but it misleadingly claims 'no arguments' while the schema defines three optional parameters. It also fails to state side effects, prerequisites like a prior project scan, or output location.

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 efficiently structured into three concise sentences, with the core action front-loaded. Each clause adds relevant context (audience, auto-population, integrity hash, compliance date, plan requirement). The only minor flaw is the misleading 'no arguments' phrase, which costs a point.

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

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

The description gives a high-level overview of purpose and constraints but omits operational details such as where the package is saved, what file format is produced, and whether a previous scan_project call is required. With no output schema and no annotations, these gaps leave the agent without complete invocation context.

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

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

The input schema provides full descriptions for all three parameters, which would normally warrant a baseline of 3. However, the description's 'no arguments' statement actively contradicts and muddies the schema, adding no value and potentially confusing users into thinking parameters don't exist.

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: 'Build the Annex IV evidence package' and immediately identifies the audience ('your auditor') and the context ('high-risk AI'). It also highlights the deliverable's 8 mandatory sections, distinguishing it from sibling compliance and reporting 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 implies usage for high-risk AI audits and notes the Aug 2026 applicability, giving context for when the tool is relevant. However, it does not explicitly state when not to use it or mention alternatives like check_compliance or generate_report, so it lacks explicit exclusions and 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, the description carries the burden of disclosing behavior. It does so by revealing that the tool auto-scans the project, ranks fixes by impact, sequences quick wins, and evaluates deadline feasibility. It also mentions the Pro plan restriction, which is a useful behavioral caveat. However, it does not mention any potential side effects or whether the operation is read-only, though its nature as a planner suggests it is.

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 long and front-loaded with the primary value proposition. It provides key information (auto-scan, ranking, sequencing, deadline feasibility, pricing) without unnecessary verbosity. Every sentence 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?

The description sufficiently covers the tool's purpose, behavior, and constraints. It does not explain the return format, but no output schema exists, so the description is the only source of return behavior—it mentions 'tells you whether your deadline is achievable' which gives a hint. Given the optional parameters are well-documented in the schema, the description provides adequate context for a tool of this complexity.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds no parameter-specific semantics beyond what the schema already provides. The phrase 'no arguments' is technically misleading since the schema has optional parameters, but this does not impact the semantics of individual parameters.

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

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

The description clearly states the tool's function: generating a prioritized, week-by-week compliance roadmap. It uses a specific verb ('Get') and resource ('compliance roadmap') and distinguishes itself from check_compliance by positioning that as a free gap summary. The outcome is concrete: closing every compliance gap before a deadline.

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 a condition for use ('Pro plan required') and names an alternative ('run check_compliance() for a free gap summary'). This gives clear guidance on when to use this tool versus a sibling, fulfilling the 'alternatives' requirement.

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 discloses that templates are pre-filled, tailored to risk category, and highlights that high-risk systems get all 6 mandatory documents. It also instructs to 'Save to docs/, fill in [bracketed] sections,' conveying the output format and intended use. It does not mention potential side effects like overwriting files, but the behavior is clearly outlined.

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. The first sentence immediately conveys the core value proposition, and the second sentence adds essential usage details (save location, fill-in instructions, prerequisite). Every sentence earns its place, with no redundant or vague language.

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 moderate complexity (single parameter, no output schema, no annotations), the description provides sufficient context: what the tool does, how to use it (save/fill), and a workflow hint (run check_compliance first). It doesn't discuss edge cases like unacceptable risk or all possible document types, but it covers the main usage adequately 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?

The input schema already provides a detailed description for risk_category ('EU AI Act risk category. Templates are most useful for high risk.'), achieving 100% coverage. The tool description adds further meaning by specifying that 'High-risk systems get all 6 mandatory documents,' implying that different categories produce different outputs. This goes beyond the schema's baseline.

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 pre-filled templates for risk management, technical documentation, transparency notice, and human oversight policy.' It also specifies the resource (templates) and scope (tailored to risk category, with high-risk systems receiving 'all 6 mandatory documents'), distinguishing it from sibling tools like generate_report or 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?

The description explicitly tells the user to 'Run check_compliance() first to see which documents you're missing,' providing a clear prerequisite and workflow. It implies usage for EU AI Act compliance template generation and differentiates from manual writing ('Stop writing compliance docs from scratch'), but it does not explicitly list alternative tools when not to use.

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

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

With no annotations provided, the description carries the behavioral transparency burden. It discloses that one call auto-detects AI frameworks, runs gap analysis, produces a structured plan, and requires no API key—valuable operational details. It does not address side effects or error cases, but for a read/analysis tool this is sufficient.

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

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

Two sentences front-load the core purpose and immediately follow with the value proposition. Every clause adds information (target audience, zero-config, auto-detection, output artifact, auth requirement), with no filler or 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?

The tool's simplicity (two optional params, no output schema) is well matched by a description that explains purpose, behavior, and output. The only minor gap is not explicitly mentioning the optional parameters' existence, but the schema covers them. Overall, an agent can confidently invoke this tool without further clarification.

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?

Both parameters are fully documented in the schema with descriptions and defaults (100% coverage), so the baseline is 3. The description adds slight semantic value by emphasizing 'no arguments' and the auto-detection behavior, implying parameters are optional conveniences. It does not elaborate on how risk_category influences the report beyond the schema's enum explanation.

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

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

The description uses a specific verb-resource pair ('Generate the compliance report') and extends the purpose with concrete deliverables (gap analysis, structured remediation plan). It distinguishes itself as a zero-argument report tool aimed at legal review, which differentiates it from siblings like gdpr_generate_report or generate_compliance_roadmap.

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 a use case ('your legal team is asking for') and notes 'no arguments' and 'No API key needed', but it never explicitly contrasts with sibling report tools (e.g., combined_compliance_report, gdpr_generate_report) or states when to choose an alternative. This leaves the selection decision to the agent without clear guidelines.

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 the tool's output content (plan details, prices, features), states 'No arguments,' and hints at dependency on API key activation via register_free_key. While it doesn't explicitly say 'read-only' or list auth requirements, the behavior is clear: it simply displays pricing information without 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: the first clause states the purpose, followed by a dash-separated plan summary, and ends with a useful cross-reference to register_free_key. Every sentence serves a purpose, and the entire description is just two sentences 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?

Given the tool's simplicity (zero parameters, no output schema, a simple informational task), the description is complete. It explains what the user will see (plan details), confirms no arguments are needed, and offers a relevant next step for API key activation. There are no gaps for the agent to misinterpret.

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, so baseline is 4. The description reinforces this with 'No arguments,' which is all that is needed. It adds no additional parameter semantics because there are none to explain.

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 and resource: 'See pricing and features for every plan.' It enumerates the plans, which distinguishes it from all sibling tools that deal with compliance, scanning, or key management. No ambiguity about what this tool does.

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 by stating 'See pricing and features' and then gives concrete next steps with 'Call register_free_key() to activate your free API key.' This provides clear context for when to use the tool, though it does not explicitly mention alternatives or exclusions. Since no sibling tool offers pricing, this is effective 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?

With no annotations, the description carries the full burden. It discloses the effect ('unlocks scan history and CI/CD integration') and clarifies input constraints ('no password or credit card'). It also adds a critical behavioral warning about not passing fabricated emails. While it does not discuss reversibility or potential side effects, it goes beyond a minimal mutation 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 concise, front-loaded with the main purpose, and every clause carries essential information. The important warning about email sourcing is highlighted with 'IMPORTANT' without excessive 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?

For a simple one-parameter tool with no output schema, the description covers the core action, expected effects, and a critical interaction requirement. It does not explain the response format or error cases, but the tool's simplicity and clear guidance make it sufficiently complete for an agent to use 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 coverage is 100% for the single email parameter, and the schema already states the email must come from the user's message. The description adds operational detail ('ask the user to type their email first, wait for their reply') and reinforces the prohibition on fabricated emails, which is valuable for agent behavior beyond the schema's static description.

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

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

The description uses a specific verb ('Activate') and identifies the resource ('free API key') with a concrete outcome ('unlocks scan history and CI/CD integration'). It clearly distinguishes from sibling tools like validate_api_key and get_pricing by focusing on activation rather than validation or pricing.

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 context for when to use the tool: after the user has provided their email. It includes an explicit workflow ('ask the user to type their email first, wait for their reply, then call this'). However, it does not explicitly mention alternatives or edge cases (e.g., when to use validate_api_key instead), 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 covers key behaviors: it is a fast scan (5 seconds), requires no arguments or setup, scans across 22 frameworks, and returns a risk category plus required legal actions. It does not mention side effects or network usage, but for a read-only scan tool these omissions are minor, so a slight deduction from full marks is appropriate.

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; the first sentence states the core value proposition, the second explains what it scans and returns, and the third gives a necessary alternative. No unnecessary words or 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?

For a tool with zero required parameters and full schema coverage, the description provides a complete picture: purpose, behavior, output, and a clear alternative. The return value is described as 'risk category and the legal actions required', which is sufficient without an output schema.

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

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

The input schema already provides descriptions for both parameters (100% coverage). The description adds little beyond saying 'no arguments', which is consistent with both parameters having defaults. Since the schema handles parameter semantics, the baseline of 3 applies.

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: 'Find out in 5 seconds if your project triggers EU AI Act obligations' and specifies it scans for 22 AI/ML frameworks. It distinguishes itself from siblings by explicitly telling users to call combined_compliance_report instead for EU AI Act + GDPR combined.

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?

Explicit usage guidance is provided: 'For EU AI Act + GDPR together, call combined_compliance_report() instead.' This directly tells the agent when not to use this tool and offers a clear alternative, satisfying the when/when-not criterion.

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?

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It discloses the output (category, confidence, indicators, articles, first step) and key constraints (no project scan, no API key). It does not mention potential caveats like the assessment being preliminary or non-authoritative, but it gives a clear picture of what to expect.

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: it immediately tells the user what to do and what to expect. Every sentence adds value (input, output, constraints) without repetition or filler.

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 one-parameter tool with no output schema, the description adequately covers input requirements and output contents, including the specific fields returned (category, confidence, indicators). It lacks details on the format of articles and compliance steps, but these are secondary to the core purpose.

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 covers 100% of the parameter with a clear description and example. The tool description reinforces 'plain language' but adds no new meaning beyond the schema, so the baseline of 3 applies.

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: it takes a plain-language description of an AI system and returns the EU AI Act risk tier, applicable articles, and first compliance step. It further specifies the returned data (matched category, confidence level, triggering indicators) and distinguishes itself from siblings with 'No project scan needed, no API key.'

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

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

The description implies when to use this tool: when you have a system description and need a quick risk classification. It contrasts with project-scanning siblings by noting no scan or API key is required, which provides useful context. However, it does not explicitly state alternative tools for other use cases or provide explicit '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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It does explain the return output (plan tier, email, usage stats), which is useful. However, it does not explicitly indicate whether the operation is read-only or what happens for invalid keys, leaving some behavioral 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 a single, front-loaded sentence that immediately states the action and then enumerates the return data. Every word is informative and there is no wasted text.

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 one-parameter tool with no output schema, the description is complete enough: it states what the tool does and what the user gets back. It does not specify error handling, but the scope is small and the return values are well covered. A 4 is appropriate.

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 parameter 'api_key' is already well-documented. The description does not add any new parameter-specific meaning beyond what the schema provides, matching the baseline score of 3 for high coverage.

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

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

The description clearly states the tool's purpose with a specific verb ('Check') and resource ('your API key status') and lists specific return values (plan tier, email, usage stats). It distinguishes itself from siblings like register_free_key and get_pricing by focusing on validation of an existing key rather than registration or pricing.

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

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

The description implies when to use this tool: when you need to check an API key's status. It provides clear context (checking status) but does not explicitly mention alternatives or when not to use it. 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.