Der einzige MCP-Server, der speziell für KI-Agenten entwickelt wurde, die in Europa verkaufen.
Von Elisabeth Hitz — über 5 Jahre EMEA-Enterprise-Vertrieb bei Deel (12 Mrd. $), Autodesk, Criteo, Red Points.
Die meisten KI-SDR-Plattformen (11x, Artisan, Alta, Landbase) wurden von US-Gründern für US-Käufer entwickelt. Sie scheitern in dem Moment, in dem sie den Atlantik überqueren.
EMEA ist kein einheitlicher Markt. Der britische Käufer will Daten. Der deutsche Käufer will Dokumentation und Prozesse. Der spanische Käufer braucht Vertrauen über einen längeren Zeitraum. Der französische Käufer erwartet intellektuelle Strenge — auf Französisch. Der niederländische Käufer möchte, dass Sie im ersten Satz auf den Punkt kommen.
Generische Ansprachen funktionieren in EMEA nicht nur schlecht — sie schädigen aktiv das Markenvertrauen, und wenn das Vertrauen auf einem europäischen Markt erst einmal weg ist, bleibt es das für Jahre.
Dieses MCP gibt Ihrem KI-Agenten die lokalisierte menschliche Intelligenz, die er durch das Scrapen des Internets nicht erhalten kann.
Related MCP server: Agent Policy Gateway MCP Server
Tool | Was es tut |
| Vollständiges Länder-Playbook: Käuferpsychologie, was funktioniert/was nicht, Verkaufszyklus, Entscheidungsträger, Compliance, saisonale Warnungen. Vor jedem Outreach ausführen. |
| Länder- × Kanal-Vorlagen (Cold Email, LinkedIn DM, InMail, Follow-ups, Abbruch). An lokale Normen angepasst. |
| Länderspezifisches Timing: NL möchte 2-Tage-Ja/Nein-Abstände, DE möchte 4-7 Tage formelle Dokumente, Nordics möchten 5-7 Tage Bescheidenheit. |
| DSGVO + länderspezifisches Recht: UK GDPR, Impressum (DE), Mentions légales (FR), CNIL (FR), AEPD (ES), DPC (IE). Risiken, Bußgelder, Safe-Harbor-Checklisten. |
| Navigation mit mehreren Stakeholdern nach Unternehmensphase (frühes Startup / Wachstum / Mittelstand / Enterprise). Entscheidungsträger, Influencer, Blocker, Skript zum Aufbau von Champions. |
| Entwickelt aus über 3 Jahren EOR-Vertrieb bei Deel und Multiplier. Die 5 häufigsten Einwände mit länderspezifischen Antworten. |
| Alles zusammengefasst — für Feinabstimmung oder vollständigen Agenten-Kontext. |
🇬🇧 UK — Analytisch, trockener Humor, 2-4 Wochen SMB-Zyklus, UK GDPR 🇮🇪 Irland — Beziehungsfokussiert, Dubliner Tech-Kontext, EU-DSGVO 🇪🇸 Spanien — Geduldig, hierarchisch, 4-8 Wochen, August-Flaute, AEPD 🇩🇪 Deutschland — Prozessorientiert, formelle Anrede, 6-12 Wochen, BDSG + Impressum erforderlich 🇫🇷 Frankreich — Intellektuelle Strenge, französische Sprache, CNIL am strengsten bei Cookies 🇳🇱 Niederlande — Direkt, schnell (2-4 Wochen), AVG 🇸🇪 Nordics (SE/NO/DK/FI) — Bescheiden, Konsens, 3-6 Wochen, Fokus auf Nachhaltigkeit
Premium-Positionierung — länderspezifische Intelligenz ist für KI-Agenten, die europäische Käufer bedienen, unersetzlich.
Event | Preis |
Länder-Briefing | 0,10 $ |
Outreach-Vorlage | 0,05 $ |
Compliance-Check | 0,10 $ |
Stakeholder-Map | 0,10 $ |
EOR-Einwandbehandlung | 0,10 $ |
Vollständiges EMEA-Paket | 1,00 $ |
Die ersten 5 Aufrufe sind kostenlos — testen Sie es auf Claude Desktop, Cursor oder einem beliebigen MCP-Client.
Verkauf nach Deutschland:
Agent task: First touch with German prospect at €200M company
→ get_country_brief({ country: "germany" })
→ Returns: "Sehr geehrte/r Herr/Frau format. Include Impressum and Datenschutzerklärung
link. Expect 6-12 week cycle. Send detailed proposal with verifiable case study,
never overpromise. Avoid casual tone until invited."
→ get_outreach_template({ country: "germany", channel: "cold_email" })
→ Returns: Properly formatted German formal email with Sehr geehrte/r address,
GDPR-compliant signature, attached case study reference.Enterprise-Deal mit mehreren Stakeholdern:
Agent task: Mapping a 1500-employee French enterprise prospect
→ get_stakeholder_map({ company_stage: "enterprise" })
→ Returns: "C-suite sign-off, 5-10 stakeholders typical. Include Project lead + Legal
+ Procurement + IT + Data Protection Officer (EU). 3-9 month cycle. Map the org
BEFORE outreach. Champion-building script: 'Most enterprise decisions involve
5-10 stakeholders. Can I share what's typical at companies your size...'"EOR-Einwand in Echtzeit:
Prospect (German HR Director): "We'll just hire as contractors instead of EOR"
→ get_eor_objection_handler({ eor_objection: "use_contractors_instead" })
→ Returns: "In Germany, Spain, France — misclassification penalties can be 5-10x
what you saved. Back-payment of social contributions plus fines plus retroactive
employee rights. EOR removes that risk entirely. Country-specific legal framework:
Germany's AÜG / France's portage salarial..."Jedes Länder-Briefing enthält:
Anwendbarer Rahmen (DSGVO + nationales Recht)
Rechtmäßigkeit von Cold Emails (Analyse des berechtigten Interesses)
Erforderliche Outreach-Elemente (Impressum, Mentions légales, Opt-out)
Cookie-Zustimmungsregeln
Name der Aufsichtsbehörde + Risikostufe
Maximale Bußgeldrahmen
Safe-Harbor-Checkliste
Haftungsausschluss: Dies ist eine allgemeine Orientierung, keine Rechtsberatung. Lassen Sie Ihren endgültigen Prozess immer von einem qualifizierten Datenschutzanwalt prüfen. Aber es bringt Sie mit einem Tool-Aufruf zu 80 % ans Ziel.
KI-SDR-Plattformen (11x, Artisan, Alta, Landbase), die über die USA/UK hinaus expandieren wollen
Outbound-Automatisierungstools mit europäischen Kunden, die fragen: „Warum unterstützt ihr DE/FR/ES nicht richtig?"
EU-basierte KI-Agenten-Entwickler, die integrierte Compliance-Standards benötigen
Globale EOR/Payroll-KI (Verkauf für Deel/Remote/Velocity Global), die länderspezifische Einwandbehandlungen benötigen
CRM-KI-Assistenten, die autonomes Outreach über mehrere EU-Märkte hinweg betreiben
Gründer, die EU-Vertriebstools bauen und keinen regionalen Berater einstellen möchten
Generische KI-SDRs behandeln "EMEA" als eine Einheit. Die Realität:
Metrik | UK | Deutschland | Spanien | Niederlande |
Käuferstil | Direkt | Formell | Beziehungsorientiert | Hyper-direkt |
Ø SMB-Zyklus | 2-4 Wochen | 6-12 Wochen | 4-8 Wochen | 2-4 Wochen |
Kontakt-Toleranz | 3 in 2 Wochen | 4-7 Tage Abstand | 5+ Kontakte OK | max. 2-3 |
Cookie-Gesetz | PECR | Am strengsten in EU | AEPD aktiv | AVG |
Schlechteste Zeit | Aug + Weihnachten | Schul-/Sommerferien | August (tot) | Jul-Aug |
Das kann man nicht aus einem Blogbeitrag kodieren. Es stammt aus über 5 Jahren praktischer Vertriebserfahrung bei Deel (12 Mrd. $), Autodesk, Criteo, Red Points.
Funktioniert mit jedem MCP-kompatiblen Client:
Claude Desktop
Cursor
Cline
Windsurf
Benutzerdefinierte MCP-Implementierungen
{
"mcpServers": {
"emea-compliance": {
"command": "node",
"args": ["/path/to/emea-compliance-mcp/dist/main.js"]
}
}
}Wenn Sie 11x, Artisan, Alta, Landbase, Outreach, Apollo sind oder ein ähnliches Produkt bauen und EMEA-Funktionen für Ihre Kunden als White-Label anbieten möchten, schreiben Sie mir auf LinkedIn. White-Label-Deals + kundenspezifische Ländererweiterungen verfügbar.
Elisabeth Hitz — Schweizerisch-amerikanische B2B-Vertriebsleiterin mit Sitz in Barcelona.
Über 5 Jahre EMEA-Enterprise-Vertrieb
Deel (12 Mrd. $ Bewertung), Autodesk, Criteo (268 % Quote), Red Points
Erfolgreiche Deals in UK, Deutschland, Spanien, Frankreich, Irland, Niederlande
Muttersprachlerin Schweizerdeutsch, fließend Französisch/Spanisch, professionelles Englisch
Baut jetzt closermethod.com und den EMEA-KI-Agenten-Stack auf
LinkedIn: linkedin.com/in/elisabethhitz
MIT.
Get country-specific compliance requirements for cold outreach: GDPR framework, cold email legality, required elements (Impressum for Germany, Mentions légales for France, etc.), cookie consent rules, regulator names, fine ranges. Critical for AI agents doing autonomous outreach in EU/UK.
| Name | Required | Description | Default |
|---|---|---|---|
| country | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavior. It effectively describes the return values (GDPR framework, legality, required elements, etc.), implying a read-only lookup. Although it does not explicitly state it is non-destructive, the content suggests informational retrieval.
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 concisely convey the tool's purpose and key information. The first lists deliverables, the second adds context. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple lookup tool with one parameter and no output schema, the description fully covers the output (GDPR, legality, elements, cookie consent, regulators, fines) and usage context (autonomous outreach in EU/UK).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter with enum values, and schema description coverage is 0%, so the description must add meaning. It mentions countries in context (EU/UK, implicitly the enum values) and explains what compliance info is returned per country, adding value beyond the bare enum list.
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 specifies the verb 'get' and resource 'country-specific compliance requirements for cold outreach', listing concrete items like GDPR, legality, and required elements. It clearly distinguishes from siblings like get_country_brief by focusing on compliance details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states the tool is 'Critical for AI agents doing autonomous outreach in EU/UK', clearly indicating when to use it. However, it does not explicitly mention when not to use it or compare with alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get a complete country-specific buyer psychology brief including communication style, what works/kills deals, sales cycle expectations, decision makers, compliance notes, and seasonal warnings. Use this before any outreach to a specific EMEA market.
| Name | Required | Description | Default |
|---|---|---|---|
| country | Yes | EMEA country to get brief for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. However, it only lists the brief's contents (communication style, what works/kills deals, etc.) without mentioning if the operation is read-only, requires authentication, has rate limits, or what the return format is. For a tool with zero annotation coverage, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the purpose and a list of included items, followed by a usage recommendation. Every sentence contributes value without redundancy or verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given low complexity (one required parameter, no nested objects, no output schema), the description covers the tool's purpose and contents well. It explains what the brief includes, which compensates for the lack of output schema. Minor omission: could mention return type or format, but overall complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage for the single parameter (country), and the description merely echoes 'EMEA market' without adding new meaning. Per guidelines, baseline is 3 when schema_coverage is high, and the description adds no extra value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves a 'complete country-specific buyer psychology brief' with explicit list of contents (communication style, deal dynamics, etc.). The verb 'Get' is specific, and the resource is well-defined, distinguishing it from sibling tools like get_compliance_check or get_followup_cadence which target narrower aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage context: 'Use this before any outreach to a specific EMEA market.' It implies this is an initial step, but does not explicitly exclude scenarios or mention when to use sibling tools instead. The guidance is present but lacks comparative alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Handle EOR (Employer of Record) and global hiring objections with country-specific responses. Built from 3+ years selling EOR at Deel and Multiplier across EMEA. Covers: 'use contractors instead', 'not ready to commit', 'too expensive', 'we have entity', 'compliance unclear'.
| Name | Required | Description | Default |
|---|---|---|---|
| eor_objection | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It mentions the source of the responses but does not describe side effects, authentication requirements, rate limits, or whether the tool is read-only. This is insufficient for safe invocation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with three sentences that front-load the core purpose. Each sentence adds value (purpose, credibility, coverage). No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and only one simple parameter, the description still fails to explain what the tool returns or how the handler is invoked. It does not cover expected output format or behavior, leaving the agent without sufficient context to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter with an enum of 5 values and 0% schema description coverage. The description lists similar objection labels but does not provide additional meaning about each value's context or expected usage. The parameter semantics are only marginally clarified.
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 handles EOR objections with country-specific responses, listing five specific objection types. This distinguishes it from sibling tools like 'get_compliance_check' or 'get_outreach_template', which address different sales support needs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when encountering EOR objections by listing covered scenarios, but it lacks explicit when-to-use or when-not-to-use instructions, and does not mention alternative tools for related tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the country-specific follow-up cadence with day-by-day sequence and rules. EMEA markets vary dramatically: Netherlands wants 2-day gaps with yes/no questions, Germany wants formal documented 4-7 day gaps, Nordics want 5-7 day modest pacing.
| Name | Required | Description | Default |
|---|---|---|---|
| country | Yes |
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 discloses that the cadence varies by country with specific gaps and question styles, but does not cover other behavioral aspects like permission requirements or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences: the first states the purpose, the second provides illustrative context. No unnecessary words, and it front-loads the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity (single parameter, no output schema), the description is largely complete. It explains the tool's output and regional variations, though it could detail the return format of the 'day-by-day sequence and rules'.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one enum parameter 'country' with 0% description coverage. The tool description adds meaningful context by explaining how the country influences the cadence, providing concrete examples beyond the enum list.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'get' and the resource 'follow-up cadence' with specifics like 'country-specific', 'day-by-day sequence and rules'. It distinguishes from sibling tools such as 'get_country_brief' and 'get_compliance_check' which target different information.
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 implicit guidance by detailing country-specific examples (Netherlands, Germany, Nordics), signaling when to use this tool to retrieve tailored cadences. However, it does not explicitly mention when not to use or list alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the complete EMEA pack — all country briefs, all templates, all compliance requirements, stakeholder maps, and EOR handlers. Use to fine-tune your AI agent or load as system context for full EMEA capability.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose any behavioral traits such as side effects, rate limits, or that it is a read-only operation. The burden is on the description but it is minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loads the purpose, and includes a clear usage suggestion with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no parameters and no output schema, the description adequately explains what the pack contains. However, it lacks details on output format or size, leaving some gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has no parameters, so the baseline is 4. The description does not need to add parameter information, and it correctly omits it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves a comprehensive EMEA pack with specified components, distinguishing it from sibling tools that retrieve individual items.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description suggests using it for fine-tuning AI agents or loading as system context, which implies broad usage. However, it doesn't explicitly contrast with siblings or state 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.
Get a country-specific, channel-specific outreach template (cold email, LinkedIn DM, InMail, follow-ups, breakup). Templates are calibrated to local norms — formal address for Germany, French language for France, direct yes/no for Netherlands, etc.
| Name | Required | Description | Default |
|---|---|---|---|
| country | Yes | ||
| channel | Yes |
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 describes the core function (returning a template) but does not disclose behavioral traits such as idempotency, side effects (none expected), or access restrictions. The description is honest but lacks depth beyond the purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence of 30 words, front-loaded with the key action. Every part adds value: the verb, resource, scope (country/channel specific), and examples. No unnecessary words or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has only 2 parameters with enums and no output schema, the description covers the core purpose and parameter rationale. It does not explicitly state the return format (e.g., text content), but that is inferable. Compared to siblings, it stands alone as a focused retrieval tool; however, a brief note on output would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, meaning the description must compensate. It adds meaningful context by explaining why country and channel matter (e.g., country calibrates formality, channel determines type like cold_email vs breakup). This goes beyond the enum lists in the schema, helping the agent understand parameter significance.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and identifies the resource as a 'country-specific, channel-specific outreach template', making the purpose immediately obvious. It also provides specific examples of channel types (cold email, LinkedIn DM, etc.) and calibration details (formal address for Germany, French for France), which distinguish it from generic template 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 obtaining localized outreach templates but does not provide explicit guidance on when to use this tool versus the sibling tools (e.g., get_followup_cadence, get_full_emea_pack). There are no when-not-to-use directives or alternative recommendations, leaving the agent to infer context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the multi-stakeholder navigation map for a buyer org by company stage. Shows decision maker, influencer, common blockers, average cycle, deal size range, and champion-building script. Use before starting any deal to know who you really need to convince.
| Name | Required | Description | Default |
|---|---|---|---|
| company_stage | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description implies a read operation by saying 'Shows' and lists returned items. No mention of side effects, permissions, or data freshness, but acceptable for a simple retrieval tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short, front-loaded sentences with no waste. Efficiently communicates purpose and usage.
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 1-parameter tool with no output schema, the description covers purpose, returned content, and usage timing. Slightly missing details on output structure but adequate given low complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and the description only mentions 'by company stage' without explaining the enum values. The schema defines the enum but description adds minimal extra meaning beyond the parameter name.
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 a multi-stakeholder navigation map for a buyer org by company stage, listing specific content. Different from sibling tools which cover compliance, country info, etc.
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?
Explicitly recommends using 'before starting any deal', providing clear context. Does not describe when not to use or alternatives, but the recommendation is strong.
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.
v1.0.0get_compliance_checkget_country_briefget_eor_objection_handlerget_followup_cadenceget_full_emea_packget_outreach_templateget_stakeholder_mapScored across 7 tools
Each tool targets a distinct aspect of EMEA business outreach: compliance, country briefs, objections, follow-ups, templates, stakeholder maps, and a comprehensive pack. No overlaps.
All tools follow a consistent 'get_' prefix with descriptive snake_case nouns (e.g., get_compliance_check, get_outreach_template), making patterns predictable.
7 tools is within the ideal 3-15 range, covering the necessary functionalities without overloading or undercovering the domain.
The tools cover core needs for EMEA outreach: compliance, cultural briefing, objections, cadences, templates, and stakeholder maps. Minor gaps like real-time regulation updates or tool to update data exist but are not critical for the stated purpose.
EU compliance checks for AI agents: sanctions, company, VAT ID, IBAN, email. Pay per call.
European business verification for AI agents: registry, VAT, sanctions, IBAN. Pay-per-call x402.
Sweden-based, EU-sovereign agentic Google Business Profile SaaS. Frankfurt, Stockholm, AI Pact.
EU/UK VAT compliance for AI agents: number validation, rate lookups, reverse-charge checks.
