Next Role MCP Proxy
NextRole MCP-Proxy
Ein Model Context Protocol (MCP)-Proxyserver, der Zugriff auf die professionellen Lebenslauf- und Anschreiben-Anpassungsdienste von NextRole bietet. Dieser Proxy ermöglicht es MCP-kompatiblen Clients, mit den gehosteten Diensten von NextRole zu interagieren.
Funktionen
Professionelle Lebenslauf-Anpassung: Passen Sie Ihren Lebenslauf für spezifische Stellenbewerbungen an
Anschreiben-Erstellung: Erstellen Sie maßgeschneiderte Anschreiben, die den Stellenanforderungen entsprechen
Mehrere Dienstleistungsstufen: Professionelle Dienstleistungen für Einsteiger, Fortgeschrittene und Führungskräfte
Guthabenverwaltung: Verfolgen und verwalten Sie Ihre Dienstleistungsguthaben
Internationale Unterstützung: Verfügbar für Benutzer weltweit
Related MCP server: LinkedIn MCP Server
Installation
Aus dem Quellcode
Klonen Sie das Repository und führen Sie das Installationsskript aus. Es installiert Abhängigkeiten, erstellt das Projekt und gibt das MCP-Client-Konfigurations-JSON für Sie aus.
git clone https://github.com/bats64mgutsi/nextrole-mcp-proxy.git
cd nextrole-mcp-proxyLinux / macOS:
bash install.shWindows (PowerShell):
.\install.ps1Am Ende des Skripts sehen Sie das MCP-Client-Konfigurations-JSON mit dem korrekten Pfad zu Ihrer lokalen Installation. Kopieren Sie es in die Konfigurationsdatei Ihres MCP-Clients.
Mit npx (keine lokale Installation)
Fügen Sie dies zu Ihrer MCP-Client-Konfiguration hinzu:
{
"mcpServers": {
"nextrole": {
"command": "npx",
"args": ["nextrole-mcp-proxy"]
}
}
}Verwendung
Verfügbare Tools
1. get_pricing
Ermitteln Sie die verfügbaren Karrierestufen und deren Produkt-IDs. Sie müssen dies vor der Bestellung aufrufen, um die korrekte productId zu erhalten.
Verwendung:
What are your different CV tailoring packages?Antwort:
[
{
"CountryCode": "ZA",
"ServiceTier": "Entry Level",
"ProductId": 1
},
{
"CountryCode": "ZA",
"ServiceTier": "Mid Level",
"ProductId": 2
},
{
"CountryCode": "ZA",
"ServiceTier": "Senior Level",
"ProductId": 3
}
]2. get_credits
Überprüfen Sie, wie viel Guthaben ein Kunde noch hat. Jede Bestellung kostet 1 Guthaben.
Parameter:
phoneNumber(erforderlich): Telefonnummer des Kunden inklusive Ländervorwahl (z. B. +27831234567)
Verwendung:
How many credits do I have left? My phone number is +27831234567Antwort:
{
"credits": 5
}3. place_order
Geben Sie eine Bestellung für einen angepassten Lebenslauf und ein Anschreiben auf. Die Bestellung dauert in der Regel etwa 15 Minuten. Der Kunde erhält SMS-Benachrichtigungen, wenn die Bestellung bestätigt wurde und wenn die Dokumente fertig sind. Kostet 1 Guthaben pro Bestellung.
Parameter:
customerPhone(erforderlich): Telefonnummer des Kunden inklusive Ländervorwahl, muss mit '+' beginnen (z. B. +27831234567)customerFirstName(erforderlich): Vorname des KundencustomerLastName(erforderlich): Nachname des KundencvMarkdown(erforderlich): Der aktuelle Lebenslauf des Kunden im Markdown-FormatproductId(erforderlich): Die Produkt-ID, die der Karrierestufe des Kunden entspricht (rufen Sie zuerst get_pricing auf)jobDescription(erforderlich): Die vollständige Stellenbeschreibung, auf die sich der Kunde bewirbt
Verwendung:
I need to tailor my CV for a Junior Software Developer position. My phone number is +27831234567, my name is John Smith, and here's my current CV in markdown:
# John Smith
## Experience
- Junior Developer at TechCorp (2023-present)
The job description is: We are seeking a Junior Software Developer to join our team with React and Node.js experience.Antwort:
{
"orderKey": "550e8400-e29b-41d4-a716-446655440000",
"status": "success",
"message": "Order placed successfully. SMS notifications sent."
}Anwendungsbeispiele
Berufseinsteiger
Perfekt für Hochschulabsolventen und Berufsanfänger:
I'm Sarah Johnson (+44207123456) and need my CV tailored for this graduate software engineer role: Graduate Software Engineer requiring Python programming and problem-solving skills.
My current CV:
# Sarah Johnson
## Education
- Computer Science Degree, University of London (2024)
## Projects
- Built a web application using Python and FlaskKarrierewechsel
Für Fachkräfte, die die Branche wechseln:
I'm transitioning from finance to tech and need my CV (+27831112233, Jane Doe) tailored for this software developer role: Full Stack Developer position requiring JavaScript, React, and database skills.
Current CV:
# Jane Doe
## Background
- Financial Analyst at Bank Corp
- Recently completed coding bootcampFührungskraft
Für C-Level- und leitende Managementpositionen:
I'm Michael Chen from the US (+1555123456) and need my executive CV customized for this CTO role: Chief Technology Officer requiring strategic leadership and team management skills.
My current CV:
# Michael Chen
## Executive Summary
Senior Technology Leader with 15+ years experience
## Experience
- VP Engineering at Tech Startup (2020-2024)Dienstleistungsstufen
Einsteiger (Produkt-ID: 1): Für Hochschulabsolventen und Berufsanfänger
Fortgeschrittene (Produkt-ID: 2): Für erfahrene Fachkräfte mit 3-10 Jahren Berufserfahrung
Führungskräfte (Produkt-ID: 3): Für leitende Angestellte, Manager und Führungskräfte
Datenschutz & Bedingungen
Durch die Nutzung dieses Dienstes stimmen Sie den folgenden Bedingungen von NextRole zu:
Entwicklung
Erstellen
npm run buildAusführen in der Entwicklung
npm run devLokal testen
npm startArchitektur
Dies ist ein leichtgewichtiger Proxy, der MCP-Anfragen an den gehosteten Dienst von NextRole unter https://api.nextrole.co.za/firstroleprod-mcp/mcp weiterleitet. Der Proxy:
Übersetzt MCP-Protokollanfragen
Leitet sie an den gehosteten Dienst weiter
Gibt formatierte Antworten an MCP-Clients zurück
Behandelt Fehler und Verbindungsprobleme
Anforderungen
Node.js 18.0.0 oder höher
Internetverbindung, um den gehosteten Dienst von NextRole zu erreichen
Lizenz
MIT-Lizenz - siehe LICENSE-Datei für Details.
Support
Bei technischen Problemen mit diesem Proxy eröffnen Sie bitte ein Issue auf GitHub. Für dienstbezogene Fragen kontaktieren Sie bitte den NextRole-Support über deren offizielle Kanäle.
Available Tools
3 toolsget_creditsA
Check how many credits a customer has remaining. Each order to tailor a CV and cover letter costs 1 credit.
| Name | Required | Description | Default |
|---|---|---|---|
| phoneNumber | Yes | Customer phone number including country code (e.g. +27831234567) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It adds valuable domain context explaining what credits are used for (1 per CV/cover letter order), but lacks operational details like error handling, what happens if phone number not found, or caching 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?
Perfectly concise with two sentences. First states purpose immediately; second provides essential domain context about credit costs. Zero 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 simple single-parameter read operation without output schema, the description is nearly complete. It explains the credit system which is essential domain context. Minor gap: doesn't hint at return value structure or error states.
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 has 100% coverage with phoneNumber fully documented. Description mentions 'customer' which loosely maps to the parameter, but adds no additional semantics, format constraints, or examples beyond what the schema already provides. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Excellent clarity: specifies the verb 'Check', resource 'credits', and scope 'remaining'. The second sentence distinguishes the domain context (CV/cover letter tailoring) which differentiates this from generic balance checking 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?
Provides implied usage context by explaining that orders cost 1 credit, suggesting this should be checked before placing orders. However, lacks explicit when-to-use guidance or direct comparison to siblings (get_pricing, place_order).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_pricingA
Get the available career-level tiers and their product IDs. Different products are designed for different career phases, so the customer should pick the tier that best matches where they are in their career. You must call this before placing an order to get the correct productId.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
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 successfully explains the business logic (career phases) and workflow ordering (must precede place_order), but lacks technical behavioral traits such as whether the operation is idempotent, cached, or rate-limited, and provides only high-level description of return values without structural details.
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 consists of three efficiently structured sentences: the first defines the core action, the second provides business context for selection, and the third states the workflow prerequisite. Every sentence earns its place with no redundant or filler content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite lacking an output schema, the description partially compensates by explaining that the tool returns 'career-level tiers and their product IDs'. Combined with the explicit workflow integration (prerequisite for place_order), this provides sufficient context for a zero-parameter lookup tool, though specific return structure details would strengthen it further.
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 contains zero parameters. Per evaluation rules, zero-parameter tools receive a baseline score of 4. The description appropriately does not mention parameters since none 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 explicitly states the tool retrieves 'career-level tiers and their product IDs' using the specific verb 'Get'. It distinguishes itself from sibling tools by explaining its role as a prerequisite for place_order (getting productId), clearly differentiating it from get_credits.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit workflow guidance: 'You must call this before placing an order to get the correct productId.' It also includes selection criteria ('customer should pick the tier that best matches where they are in their career'), giving clear context on when and how to use the results.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
place_orderA
Place an order for a tailored CV and cover letter. The order typically takes about 15 minutes to complete. The customer will receive an SMS confirming their order and another SMS when their documents are ready to download. Costs 1 credit per order.
| Name | Required | Description | Default |
|---|---|---|---|
| customerPhone | Yes | Customer phone number including country code, must start with '+' (e.g. +27831234567). SMS notifications will be sent to this number. | |
| customerFirstName | Yes | Customer's first name | |
| customerLastName | Yes | Customer's last name | |
| cvMarkdown | Yes | The customer's current CV in markdown format. This is used as the basis for tailoring their documents. | |
| productId | Yes | The product ID that matches the customer's career level. Call get_pricing first to see available career-level tiers and their product IDs. | |
| jobDescription | Yes | The full job description the customer is applying for. The CV and cover letter will be tailored to match this role. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden, effectively disclosing key behavioral traits: processing time (~15 minutes), notification mechanism (two SMS messages), and cost (1 credit). It omits idempotency or error handling details.
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?
Four tightly constructed sentences with zero waste: purpose, timing, notifications, and cost. Information is front-loaded and 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?
Given the 6-parameter complexity and lack of annotations/output schema, the description is reasonably complete, covering the user journey (order → SMS confirmation → SMS completion). It could strengthen by noting the prerequisite check for credits.
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, establishing a baseline of 3. The description text does not add parameter-specific semantics (e.g., explaining markdown format or productId sourcing), relying entirely on 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 opens with a specific verb ('Place') and clear resource ('order for a tailored CV and cover letter'), immediately distinguishing it from the read-only sibling tools get_credits and get_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 mentions 'Costs 1 credit per order,' implying a prerequisite to check credits, but lacks explicit guidance on when to use versus alternatives or a required workflow (e.g., calling get_pricing first to obtain the productId).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
3 tool updates
v0.1.0- First observed
get_credits - First observed
get_pricing - First observed
place_order
TDQS
Each tool serves a distinct purpose with clear boundaries: get_credits checks account balance, get_pricing retrieves product catalog, and place_order executes transactions. No functional overlap exists between the three operations.
All tools follow a consistent verb_noun snake_case convention. The naming clearly distinguishes between retrieval operations (get_) and the transactional operation (place_).
Three tools is minimal but reasonable for a focused ordering workflow. While the surface is thin, it covers the essential path from balance check to order completion without unnecessary bloat.
The toolset supports order creation but lacks order management capabilities such as status checking, order history retrieval, or cancellation. Once place_order is called, the agent has no visibility into order progress, creating a dead end for follow-up queries.
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