EU regulatory compliance data: 17 regulations, deadlines, enforcement actions. EU-hosted.
Scored across 12 tools
Most tools target clearly distinct resources/actions: single-question answering (answer_procurement_question) vs batch filling (autofill_security_questionnaire), architecture audit (audit_local_ai_stack) vs code-diff audit (audit_repo_pull_request), and list vs single lookup for regulations. The pair answer_procurement_question/autofill_security_questionnaire and the three commercial packs/license tools have mild overlap but descriptions disambiguate them well.
Most tools follow a clean verb_noun pattern (answer_, audit_, autofill_, generate_, get_, list_, verify_). Two tools break the pattern: regulation_lookup uses noun_verb ordering and upcoming_deadlines has no verb at all, but overall the convention is readable and largely predictable.
At 12 tools the set is well within the ideal band and each tool maps to a distinct capability across regulations, audits, questionnaires, dossiers, and licensing. Slightly heavy on the commercial/licensing side, but nothing feels truly redundant.
Strong coverage of the compliance domain: regulation discovery/lookup, deadlines, enforcement actions, audits, questionnaires, and dossier generation, plus monetization tools. Minor gaps exist (no cross-regulation search or comparison tool, no pagination/filter helpers beyond the stated ones), but core workflows are covered without dead ends.
Answer an enterprise procurement, security questionnaire, or RFP question regarding EU AI Act, GDPR, or data sovereignty. Returns an authoritative, legally and technically grounded response ready to paste into OneTrust, Whistic, or client emails.
| Name | Required | Description | Default |
|---|---|---|---|
| question | Yes | The question asked by the enterprise client or procurement team (e.g. 'Do you use customer prompts for model training?', 'Where is customer data stored?', 'What is your subprocessor notice period?'). | |
| company_name | No | Name of your organisation (optional, defaults to 'Our Organisation'). |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so safety is covered. The description usefully adds that the output is a paste-ready response targeted at OneTrust, Whistic, or client emails, but it says nothing about sourcing, citation behavior, latency, or coverage limits for the legal grounding it claims.
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, zero filler: the purpose and the return-value framing are both front-loaded. Nothing in the text is redundant with the schema or annotations.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With a 2-param schema fully documented, read-only annotations, and an output schema handling return values, the description only needs to establish purpose and framing, which it does. It could be stronger by noting the legal-domain boundaries or how answers are grounded, but nothing essential for invocation 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?
Schema description coverage is 100%, so both parameters (question, company_name) are fully documented with examples and defaults in the schema. The description adds no extra parameter semantics, 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 uses a specific verb+resource ('answer... procurement, security questionnaire, or RFP question') scoped to EU AI Act, GDPR, and data sovereignty, which is clear on its own. It does not explicitly differentiate itself from the sibling regulation_lookup, which an agent might reasonably confuse with this domain-specific answering 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?
Usage is only implied: the agent must infer that this is the tool for answering third-party procurement/security questions rather than looking up regulations. There is no explicit when-to-use/when-not guidance and no mention of when regulation_lookup or list_regulations would be the better choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Audit an AI application architecture for EU AI Act risk classification, GDPR Chapter V international transfer exposure, and data sovereignty compliance. Checks provider, inference region, zero-retention flags, and use case against statutory Annex III and Art. 50 criteria.
| Name | Required | Description | Default |
|---|---|---|---|
| provider | Yes | Model provider or cloud host (e.g. 'openai', 'mistral', 'anthropic', 'aws-bedrock', 'azure-openai', 'self-hosted'). | |
| use_case | Yes | Business use case (e.g. 'customer support chatbot', 'cv screening / hiring', 'code generation', 'credit scoring'). | |
| model_name | No | Model identifier (e.g. 'gpt-4o', 'mistral-large-2407', 'claude-3-7-sonnet'). | |
| inference_region | No | Geographic hosting region (e.g. 'eu-frankfurt', 'eu-west-1', 'us-east-1', 'global'). | |
| zero_data_retention | No | Whether zero prompt data retention (store: false / opt-out) is verified. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds the meaningful behavioral fact that the assessment is rule-based against Annex III and Art. 50 criteria, but says nothing about edge cases, determinism of results, or what happens when required provider/use_case data is ambiguous.
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 tightly packed sentences with no filler, leading with the action and scope before the criteria checked. Every clause carries distinct information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema exists, so return values need not be explained, and the input schema fully documents all five parameters. The description covers purpose, scope, and the statutory basis; the only gap is explicit guidance on sibling selection, which keeps it from 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?
Schema description coverage is 100%, and the description's list of checked inputs (provider, inference region, zero-retention, use case) merely restates the schema fields rather than adding format, accepted values, or fallback behavior. Baseline 3 is appropriate when the schema carries all parameter meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb ('Audit') plus resource ('AI application architecture') and enumerates the three compliance lenses: EU AI Act risk classification, GDPR Chapter V transfer exposure, and data sovereignty. The audit/classify framing clearly separates it from the sibling lookup tools (regulation_lookup, list_regulations, get_enforcement_actions), so an agent can route to it without opening the schema.
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 it applies (an AI deployment needing classification against statutory criteria) but never states when to prefer it over siblings like regulation_lookup for raw statutory text or generate_executive_trust_dossier for a report. No exclusions or prerequisites are given, so usage is inferred rather than directed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Audit a code diff, package.json dependencies, or source code snippet for EU AI Act obligations, non-EEA cloud data transfers, and prompt retention exposure. Generates a review-ready GitHub PR comment or CI/CD compliance report.
| Name | Required | Description | Default |
|---|---|---|---|
| diff_or_code | Yes | Git diff, package.json contents, or source code snippet to audit. | |
| project_name | No | Project or repository name (optional, defaults to 'Current Project'). |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds that the tool produces a PR comment or CI/CD compliance report artifact, which is useful beyond the annotations, but an output schema exists and no auth, rate-limit, or data-handling behavior is disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, zero filler, with the audit scope front-loaded and the output artifact stated second. Every clause carries information the agent needs.
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 two-parameter, read-only tool with full schema coverage, annotations, and an output schema, the description covers inputs and outputs adequately. The only real gap is routing guidance relative to sibling audit tools, which keeps it short of 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?
Schema description coverage is 100% for both parameters, so the schema already explains that diff_or_code accepts a diff, package.json, or snippet and that project_name defaults to 'Current Project'. The description repeats the input types without adding syntax, size limits, or format constraints beyond the schema, so 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?
States a specific verb (audit) with named inputs (code diff, package.json dependencies, source snippet) and named compliance domains (EU AI Act, non-EEA transfers, prompt retention). This distinguishes it from the sibling audit_local_ai_stack, which audits a local stack rather than a diff or snippet.
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 listing accepted inputs and the PR/CI output context, so an agent can infer it is for reviewing code artifacts. However, it never states when to prefer this over audit_local_ai_stack or how it relates to the other compliance siblings, and gives no exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Auto-fill a batch of enterprise security, GDPR, and EU AI Act vendor questionnaire / RFP questions (e.g. OneTrust, SIG, Whistic, CAIQ, custom Excel). Returns structured answers with EUR-Lex statutory citations, technical proofs, and copy-pasteable memos. When no Polar license key is provided, returns free answers for the first 2 questions and a direct checkout link for the remainder.
| Name | Required | Description | Default |
|---|---|---|---|
| questions | Yes | Array of question strings from the procurement or security questionnaire. | |
| ai_provider | No | Primary AI model provider (e.g. 'Mistral AI', 'OpenAI EU', 'Self-hosted'). | |
| company_name | No | Name of your organisation (optional, defaults to 'Our Organisation'). | |
| primary_hosting | No | Primary cloud hosting location (e.g. 'Frankfurt, Germany', 'Dublin, Ireland'). | |
| polar_license_key | No | Optional Polar license key (e.g. ec_live_... or license purchased via Polar). If omitted, returns first 2 questions answered and an instant checkout link. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover readOnlyHint and idempotentHint, so the bar is lower. The description adds genuinely new behavioral context: output shape (structured answers with EUR-Lex citations, proofs, memos) and a freemium gate — only the first 2 questions are answered without a Polar license key, with a checkout link for the rest.
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 sentences, front-loaded with the core action and platform targets, followed by return-value summary and the licensing caveat. Dense but each sentence carries distinct information; no 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?
An output schema exists, so return-value explanation is optional, and the description still summarizes outputs. It covers the freemium limitation, which is the main non-obvious behavior. Missing only explicit sibling routing, which is minor given the tool's specificity.
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 all five parameters are already documented in the schema (including the polar_license_key freemium behavior). The description adds no parameter-level syntax or format guidance beyond that, 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?
States a specific verb (auto-fill) plus resource (batch of enterprise security, GDPR, and EU AI Act vendor questionnaire / RFP questions) and names concrete platform targets (OneTrust, SIG, Whistic, CAIQ). The 'batch' framing also implicitly distinguishes it from the sibling answer_procurement_question.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the use case (batch questionnaire/RFP questions) and explains the no-license fallback path, but never explicitly says when to use this over answer_procurement_question or the audit/dossier siblings, nor any prerequisites beyond the license key.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate an Executive Trust & Compliance Dossier (ETD) for B2B enterprise procurement and vendor onboarding. Includes Executive Passport, vector data flow diagram, tripartite evidence grid, and procurement approval memo. When no license key is provided, returns an executive preview and instant checkout link.
| Name | Required | Description | Default |
|---|---|---|---|
| ai_provider | No | Optional primary AI provider (default 'Mistral AI / EU Sovereign'). | |
| company_name | Yes | Name of the organisation or product. | |
| primary_hosting | No | Optional primary hosting location (default 'Frankfurt (Germany)'). | |
| polar_license_key | No | Optional Polar license key (e.g. ec_live_... or license purchased via Polar). If omitted, returns executive preview and instant unlock link. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations cover the safety profile (readOnlyHint, idempotentHint), and the description adds genuinely useful behavior the annotations do not: the no-license branch returns an executive preview plus an instant checkout link. It still does not disclose latency, rate limits, or whether generation has side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with purpose, then deliverables, then the conditional license behavior. No wasted clauses, though the enumeration of four deliverables is slightly list-heavy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema exists, so return values need not be explained, and the description still sketches the document's contents and the free-preview fallback. For a low-parameter, read-only generation tool this is close to complete; only sibling-routing guidance is absent.
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 all four parameters and their defaults (Mistral AI, Frankfurt hosting, Polar license) are already documented in the schema. The description only restates the license-key behavior, adding no syntax or format detail beyond the schema; 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?
States a specific verb + resource ('Generate an Executive Trust & Compliance Dossier') and enumerates its deliverables (Executive Passport, flow diagram, evidence grid, memo). It clearly differs in kind from the sibling regulation-lookup tools, though it never names an alternative to route against.
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 'for B2B enterprise procurement and vendor onboarding' clause implies the use context, and the license-key sentence implies a free-preview path. However, there is no explicit when-to-use guidance or comparison against siblings like answer_procurement_question or audit_local_ai_stack.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return recent EU enforcement actions ingested from official DPA publications (CNIL, Garante, ICO, AEPD, APD, Dutch AP, BfDI, EDPB, ESMA, EBA, EIOPA, CJEU, BSI, ANSSI). Filter by country, regulation, or urgency. Each result includes source authority, regulation, title, summary, urgency (high/medium/low), publication date, and official source URL. Data is sourced directly from supervisory authority RSS feeds and updated continuously.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of results to return. Defaults to 10, maximum 50. | |
| country | No | Filter by country (e.g. 'france', 'germany', 'italy', 'spain', 'netherlands', 'belgium', 'ireland'). Maps to the relevant supervisory authority. | |
| urgency | No | Filter by urgency level. 'high' = immediate action required; 'medium' = monitor closely; 'low' = informational. | |
| regulation | No | Filter by regulation keyword (e.g. 'GDPR', 'AI Act', 'NIS2', 'DORA', 'DSA'). |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, but the description adds valuable behavioral context: it specifies the data source (RSS feeds from supervisory authorities), the continuous update frequency, and the fields returned per result. This goes beyond what annotations 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 concise, using two sentences that front-load the main purpose, then detail filters and data source. Every sentence adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only tool with no required parameters and a present output schema, the description adequately covers data source, freshness, and return fields. It lacks pagination details, but those are covered in the schema, and the tool is simple enough that nothing else is needed.
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 all parameters (limit, country, urgency, regulation) are already fully described. The description merely mentions the filter options without adding new meaning, so the 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 returns recent EU enforcement actions from specific supervisory authorities, with filtering options. It distinguishes from sibling tools which focus on regulations and deadlines, making it unambiguous 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 when to use it (to retrieve enforcement actions) and lists filters, but it does not explicitly contrast with sibling tools or state when not to use. The purpose is clear enough that an agent would infer the appropriate context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate an instant 1-click checkout link for any EuroComply compliance pack, emergency deal bundle, or dataset. Pre-fills customer email for zero-friction purchase. Returns direct payment URL (Polar MoR with EU VAT/reverse-charge support), price summary, and 3-step activation instructions to use the license key in MCP.
| Name | Required | Description | Default |
|---|---|---|---|
| pack_slug | Yes | Slug or keyword for the pack. E.g. 'b2b-deal-flash-pass-2026' (or 'flash-pass'), 'ai-act-audit-shield-bundle-2026' (or 'ai-act'), 'b2b-deal-accelerator-bundle-2026' (or 'deal-accelerator'), 'dora-cyber-resilience-bundle-2026' (or 'dora'), 'eu-compliance-data-library-2026' (or 'library'). | |
| company_name | No | Optional company name to associate with the procurement memo. | |
| discount_code | No | Optional discount code to apply at checkout. | |
| customer_email | No | Optional customer email to pre-fill on the Polar checkout screen. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint and idempotentHint, so the safety profile is covered; the description goes beyond that by disclosing what is returned (payment URL via Polar MoR with EU VAT/reverse-charge, price summary, 3-step activation instructions) and the email pre-fill behavior. It does not clarify whether a checkout session is persisted server-side, which would be the remaining ambiguity for a 'link generation' call.
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 front-loaded sentences that state the action, the convenience behavior, and the return payload in that order. Minor marketing filler ('zero-friction purchase') costs it a point but nothing is buried.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema exists, so return values need not be enumerated, yet the description still summarizes them accurately and completes the picture for a 4-param tool. Only the absence of explicit usage routing to sibling tools keeps it from 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?
Schema coverage is 100%, so the schema already documents all four parameters including the pack_slug examples. The description only adds that customer_email is pre-filled and the link is '1-click', which is marginal value beyond the schema. 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?
Specific verb ('Generate') plus resource ('1-click checkout link') scoped to concrete artifacts (compliance packs, bundles, datasets). An agent can distinguish this from list_compliance_packs and verify_pack_license without opening any schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage context is implied by the description ('instant 1-click checkout link'), but there is no explicit when-to-use/when-not guidance and no routing to siblings such as verify_pack_license (post-purchase) or list_compliance_packs (discovery). The agent must infer the position in the workflow.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all EuroComply commercial packs, emergency deal bundles, and statutory workbooks available for instant purchase via Polar. Returns price in EUR, key deliverables, and instant 1-click checkout URLs. Commercial packs unlock premium evidence generation directly in MCP (e.g. full Executive Trust Dossier, unlimited vendor security questionnaire answers).
| Name | Required | Description | Default |
|---|---|---|---|
| category | No | Optional category filter: 'bundles' for outcome packages, 'datasets' for deep-dive workbooks, or 'all'. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so safety is covered. The description's added detail (EUR pricing, deliverables, checkout URLs) largely restates what the output schema supplies, and the claim that packs 'unlock premium evidence generation directly in MCP' is promotional rather than operational. It adds little behavioral context beyond structured fields.
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?
Front-loaded with the core purpose in the first clause, and the three sentences are short and ordered (what it lists, what it returns, what packs unlock). The closing upsell sentence is the only expendable part, keeping it just short of a 5.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema carrying return values, annotations covering safety, and a single optional enum parameter, the description supplies everything an agent needs: object of the listing, purchase channel, and payload contents. No material gap remains for a simple read-only catalog 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 the single 'category' parameter has its enum values fully documented in the schema. The description does not mention the filter at all, so it adds nothing here; baseline 3 is correct when the schema does all the work.
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?
Specific verb ('List') plus a precisely bounded resource (EuroComply commercial packs, emergency deal bundles, statutory workbooks) and the scope qualifier 'available for instant purchase via Polar.' An agent can tell this apart from get_pack_checkout_link or verify_pack_license, though it never names a sibling explicitly to route against.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is only implied: the agent infers this is the discovery/browse step before purchasing or verifying a pack. There is no explicit when-to-use statement, no exclusion, and no pointer to get_pack_checkout_link or verify_pack_license as the follow-up step, which would have been the natural pair for a catalog tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all EU regulations tracked by EuroComply with their short name, regulation number, CELEX, status, in-force date, application date, and a one-paragraph summary. Useful when the user wants an overview or to discover what regulations exist.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, covering the safety profile. The description adds that it returns a list with specific fields, but since an output schema exists, this is largely redundant. No additional behavioral traits (e.g., pagination, volume, or side effects) are disclosed, but none are needed given the annotation coverage. The description neither contradicts nor substantially extends the 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 a single, well-structured sentence that front-loads the verb and resource, then lists the output fields and the intended use case. Every phrase contributes meaning without waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters, an existing output schema, and annotations covering read-only and idempotent behavior, the description fully covers what an agent needs: what the tool does, what it returns, and when to use it. Nothing critical 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?
There are zero parameters, so the schema trivially covers everything. Per rubric, 0 params gives a baseline of 4. The description adds no parameter-specific information, but none is required. The lack of parameters is itself a clear signal that the tool takes no arguments.
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 ('List') and resource ('all EU regulations tracked by EuroComply'), and enumerates the returned fields. It distinguishes itself from siblings like regulation_lookup (which presumably targets a specific regulation) and enforcement actions, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear use case: 'Useful when the user wants an overview or to discover what regulations exist.' This implies when to use it versus alternatives, though it does not explicitly name the sibling tools or state when not to use it. The guidance is adequate but not exhaustive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Look up a single EU regulation tracked by EuroComply. Returns the canonical structured entry: full name, regulation number, CELEX, summary, scope, status, in-force/application dates, all key dates with Article references, max fine with tier breakdown, supervising authorities, applies-to, sector applicability, primary articles map, EUR-Lex URL, and EuroComply hub URL. Accepts the short name (e.g. 'GDPR'), full name, or slug.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Regulation identifier. One of: short name ('GDPR', 'AI Act', 'NIS 2'), regulation number ('(EU) 2016/679'), CELEX ('32016R0679'), or slug ('gdpr', 'ai-act', 'nis2', 'dora', 'cra', 'data-act', 'dma', 'dsa', 'eaa', 'pay-transparency', 'eprivacy', 'whistleblower', 'mica', 'eidas2', 'product-liability', 'csrd', 'csddd'). |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds no new behavioral disclosures such as authentication needs, rate limits, or side-effect details; the return-field list largely duplicates what the output schema already specifies. It is consistent with the annotations and mildly informative, but does not go beyond them.
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, purpose first, and no filler. The long enumeration of return fields is somewhat redundant with the output schema, but it is still compact and informative. A slightly more concise version could drop the field list and point to the schema, but the current structure remains efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter, read-only lookup with an output schema and annotations covering safety, the description is nearly complete. It explains what the tool does, what inputs are accepted, and what the response contains. It does not specify behavior when a regulation is not found or is not tracked by EuroComply, but these are minor gaps for such 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?
Schema description coverage is 100%, listing short name, regulation number, CELEX, and slug with concrete examples. The description condenses this to 'short name, full name, or slug' and introduces 'full name' as an accepted form, which is a small addition not explicitly in the schema's list. Overall the parameter meaning is already well covered by the schema, so the description adds only marginal 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 starts with a specific verb and resource: 'Look up a single EU regulation tracked by EuroComply.' It further clarifies the single-record nature by enumerating the full canonical return payload, which distinguishes it from siblings that list regulations, return enforcement actions, or surface deadlines. This is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool (when you need the canonical structured entry for one regulation), but it never names alternatives or states when not to use it. An agent must infer that list_regulations is for a full listing or that upcoming_deadlines is for time-based queries. The usage context is clear but not explicit about routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return upcoming regulatory deadlines across all tracked EU regulations within the next N days (default 365). Each result includes regulation shortName, date, event description, and Article reference. Sorted ascending by date.
| Name | Required | Description | Default |
|---|---|---|---|
| within_days | No | Look-ahead window in days from today. Defaults to 365. Maximum 1825 (5 years). |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds useful behavioral detail: the default 365-day window, the included fields (shortName, date, event description, Article reference), and ascending date sorting. This goes beyond the annotations without contradicting them.
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 with no filler. The first sentence states the core purpose and default window; the second lists return fields and ordering. 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?
For a simple read-only tool with one optional parameter, an output schema, and safety annotations, the description covers the essential details: scope, default, output fields, and sort order. Nothing critical is missing 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 single parameter within_days is fully described in the schema, including default, minimum, maximum, and meaning. The description repeats the default but adds no new semantic information beyond the schema, so the 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 states a specific verb ('Return') and a well-defined resource: upcoming regulatory deadlines across all tracked EU regulations within a look-ahead window. This clearly differentiates it from siblings like regulation_lookup, which targets a single regulation, and get_enforcement_actions, which targets enforcement actions.
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 by specifying that this covers 'all tracked EU regulations,' which implies use for cross-regulation deadline monitoring rather than single-regulation lookup. It does not explicitly name alternatives or exclusions, but the scope is clear enough for an agent to select it appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Verify a Polar license key or order transaction ID. Checks activation status and shows which premium MCP capabilities (e.g. Executive Trust Dossier, RFP Questionnaire Copilot) are active.
| Name | Required | Description | Default |
|---|---|---|---|
| No | Optional customer email associated with the purchase. | ||
| license_key | Yes | Polar license key (e.g. ec_live_..., polar_...) or Polar order ID. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds that it reports activation status and which premium capabilities are active, which is useful context about the outcome. It does not disclose what happens with an invalid/expired key, whether network calls or credentials are required, or any rate limiting.
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 tight sentences with the core action front-loaded and zero filler. The parenthetical capability examples add concrete value without bloating the 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?
An output schema exists, so return values need not be described, and the read-only, idempotent annotations cover the safety profile. The description gives enough for an agent to call it correctly; only error/edge-case behavior and the relationship to the checkout sibling are absent.
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 both parameters (license_key, email) are already documented in the schema, making 3 the baseline. The description restates that a key or order ID is accepted but adds no format or syntax detail beyond the schema, and never mentions the optional email parameter.
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?
States a specific verb and resource: 'Verify a Polar license key or order transaction ID,' then adds what the check reports (activation status and active premium capabilities). This clearly separates it from the compliance/audit siblings, none of which perform verification. It stops short of explicitly naming the adjacent sibling get_pack_checkout_link, so it earns a 4 rather than a 5.
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 never states when to call this tool versus alternatives, nor any prerequisite such as needing a key obtained from get_pack_checkout_link or the purchase flow. Usage is only implied by the verb 'Verify.' No when-not-to-use or failure-path guidance is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
get_pack_checkout_linklist_compliance_packsverify_pack_licenseaudit_repo_pull_requestautofill_security_questionnaireanswer_procurement_questionaudit_local_ai_stackgenerate_executive_trust_dossierget_enforcement_actions1 field changedOutput schema / (root)Previous value: -nullNew value: +{
+ "description": "Object with totalActionsLast90Days, filters, returned count, and array of actions (source, regulation, urgency, title, summary, publishedAt, url).",
+ "type": "object"
+}list_regulations1 field changedOutput schema / (root)Previous value: -nullNew value: +{
+ "description": "Object with count and array of regulation summaries (slug, shortName, regulationNumber, CELEX, status, dates, summary, eurlexUrl).",
+ "type": "object"
+}regulation_lookup1 field changedOutput schema / (root)Previous value: -nullNew value: +{
+ "description": "Canonical regulation entry with CELEX, key dates, max fine, supervising authorities, EUR-Lex URL, and EuroComply hub URL.",
+ "type": "object"
+}upcoming_deadlines1 field changedOutput schema / (root)Previous value: -nullNew value: +{
+ "description": "Object with today, horizon, withinDays, count, and sorted array of deadlines (date, regulation, event, articleRef).",
+ "type": "object"
+}get_enforcement_actionslist_regulationsregulation_lookupupcoming_deadlinesEU compliance corpus across 8 frameworks (NIS2, DORA, AI Act, ISO 27001 + more) via MCP.
EU regulations (GDPR, DORA, NIS2, AI Act, etc.) via Ansvar Gateway. Cited, OAuth + paid.
EU AI Act sovereignty scanning. Provider residency, registration status, audit trail support.
Verified, tier-0 regulatory data for AI across 850+ official sources and 50+ jurisdictions.
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