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CommunicationQuality Score: 34/99 (Fair)No Auth RequiredSpec vv1auto GenerationTransport: stdio

Avaza API DocumentationMCP Configuration & Schema Registry

The Avaza API Documentation Model Context Protocol (MCP) configuration provides a validated, machine-readable JSON schema and executable bridge that connects state-of-the-art AI coding assistants — including Claude Desktop, Cursor IDE, Windsurf, Cline, and VS Code Copilot — directly to the Avaza API Documentation REST API. By leveraging the standardized open Model Context Protocol, AI agents can dynamically discover capabilities, validate input parameters against strict JSON Schemas, and execute live API operations without context switching or manual copy-pasting.

Quick Specs & Integration Summary

1. Functionality:Exposes 10 API endpoints as callable AI tools for Avaza API Documentation.
2. Authentication:Zero authentication required — ready for immediate execution.
3. Protocol Layer:Standard Model Context Protocol JSON-RPC 2.0 via stdio transport.
4. Quick Launch:npx -y @modelcontextprotocol/server-openapi https://api.apis.guru/v2/specs/avaza.com/v1/swagger.json

Technical Architecture & Protocol Semantics

Under the Model Context Protocol specification, the Avaza API Documentation configuration functions as an isolated protocol adapter. When an AI agent initializes a session, the client establishes a bidirectional JSON-RPC 2.0 communication channel over standard input/output (stdio) or Server-Sent Events (SSE). During the initial handshake, the server publishes its tool manifest extracted from the Avaza API Documentation OpenAPI specification (version v1).

The Avaza API is a comprehensive suite of endpoints designed to facilitate deep integration with the Avaza platform, a modern business software solution focused on project management, resource scheduling, time tracking, and invoicing. Provided by Avaza, this API empowers developers and businesses to programmatically interact with core modules, effectively bridging Avaza's user interface with custom applications, internal workflows, and automated systems. Its primary capabilities revolve around two key domains: operational scheduling and financial management. The scheduling endpoints allow for the precise manipulation of team resources, enabling the automated creation, modification, and management of bookings and leave periods. This is invaluable for enterprises needing to synchronize Avaza's resource planner with external HR systems, calendar applications, or project management tools. Simultaneously, the financial endpoints provide robust control over the invoicing and payment cycle, allowing for the retrieval, creation, and querying of bills and bill payments. This facilitates use cases such as generating automated financial reports, syncing invoicing data with accounting software like QuickBooks or Xero, or creating custom dashboards for real-time financial monitoring, ultimately streamlining back-office operations and enhancing data visibility across the organization. When exposed as tools via the Model Context Protocol (MCP) for an AI coding assistant like Claude, the Avaza API transforms from a static documentation set into a dynamic, actionable toolkit. This integration offers significant value by contextualizing the API within a developer's immediate workflow, dramatically reducing context-switching and cognitive load. Instead of manually writing HTTP requests or parsing raw API schemas, the developer can converse naturally with the AI, which acts as a proxy to the live API. The AI assistant can leverage its understanding of the API's structure to fetch schema details, suggest correct parameter formats, and even draft boilerplate code for API calls. This turns the AI into a collaborative partner that not only explains the API but also demonstrates its practical use in real-time, accelerating development cycles and reducing integration errors by providing immediate, validated feedback on API interactions. Through an MCP server, a developer can instruct the AI agent to perform a variety of dynamic, high-value tasks that automate complex workflows. For example, a user could command, "Query all bookings for the 'Phoenix Project' team for next week and summarize them in a table," allowing the AI to use the GET /ScheduleSeries endpoints to retrieve and synthesize schedule data. Conversely, for write operations, a developer might instruct, "Based on this CSV file, add leave entries for the approved vacation list," prompting the AI to parse the data and execute multiple POST /ScheduleSeries/AddLeave calls efficiently. In the financial domain, tasks can include, "Retrieve the status of all unpaid bills older than 30 days and draft a follow-up email summary," which combines GET /api/Bill queries with text generation. Another example is, "Create a new bill for client Acme Corp with the line items from this project tracker," automating the POST /api/Bill process and ensuring data consistency between project deliverables and invoicing. While the current endpoint listing specifies "None" for authentication, the core documentation correctly emphasizes that all production integrations must adhere to strict security protocols. Developers implementing this API should prioritize setting up the appropriate OAuth2 flows (Authorization Code for server-side applications or Implicit for client-side) or generating and using Personal Access Tokens with the principle of least privilege. Every connection must be encrypted over TLS. When configuring the MCP server, these credentials should be managed securely, preferably via environment variables or a secrets manager, and never hardcoded. It is critical to grant the API token or OAuth scopes only the minimum permissions required for the application's function—for instance, a read-only token for a reporting dashboard versus a full-access token for an automation that creates bookings. Regular auditing of token usage and implementing robust error handling for authentication failures are essential best practices to maintain the security and integrity of the integrated system. This architecture guarantees strict process boundary isolation: all sensitive authorization headers and secret tokens remain sandboxed inside the client runtime, never leaking into language model context windows or external logging endpoints.

Authentication TypePublic (No Auth)Injected via local client environment
Tools & Routes Mapped10 OperationsConforms to JSON-RPC 2.0 specs
Specification OriginOpenAPI vv1auto schema validation
Documentation & Schema Quality Index
34
★ Grade C - Baseline Coverage
Automated Audit Checklist
Automated schema extraction & validation (+12 pts)
Extensive tool mapping (10 endpoints defined) (+20 pts)
Zero-configuration public API instant execution (+20 pts)
Full JSON-RPC 2.0 Model Context Protocol specification conformity (+15 pts)
Standardized endpoint summary coverage (+8 pts)

Hosted Remote Configuration URL

MCP Configuration File

Provide this hosted URL in any client that supports remote MCP schema auto-loading.

https://mcpbridge.org/config/avaza-com.json

2. AI Assistant Use Cases & Practical Workflows

Tailored for Communication

Real-world execution scenarios demonstrating how LLM agents (Claude 3.7, GPT-4o, Cursor Agent) invoke Avaza API Documentation tools to automate developer workflows.

1. Automated Incident Escalation & Notification Routing

Incident Comms

Broadcast priority notifications with rich incident context, system health metrics, and on-call engineer assignment details.

Example Natural Language Prompt:

"Dispatch a high-priority incident notification via Avaza API Documentation containing the latest stack trace, affected microservice names, and link to the active monitoring dashboard."

Mapped: /ScheduleSeries/AddBooking

2. Knowledge Base & Workspace Documentation Sync

Knowledge Sync

Synchronize newly merged pull request documentation and architectural decision records into searchable workspace hubs.

Example Natural Language Prompt:

"Fetch updated technical notes from our repository and sync them into Avaza API Documentation. Ensure headers, code blocks, and parameter tables are correctly formatted in markdown."

Mapped: /ScheduleSeries/AddLeave

3. Omnichannel Customer Ticket Triaging & Sentiment Analysis

Support Automation

Classify incoming customer inquiry tickets, detect customer sentiment urgency, and auto-draft contextual solution proposals.

Example Natural Language Prompt:

"Retrieve open customer support tickets from Avaza API Documentation. Classify urgency based on customer sentiment and generate drafted reply outlines for Tier-2 engineering review."

Autonomous Agent Loop

4. Scheduled Webhook Dispatch & Event Orchestration

Event Orchestration

Automate event notification triggers when deployments complete, staging builds pass, or schema changes are detected.

Example Natural Language Prompt:

"Configure an event notification hook in Avaza API Documentation to trigger Slack updates whenever a high-severity deployment event is logged in staging."

Autonomous Agent Loop

End-to-End Multi-Step Agent Execution Lifecycle

When an engineer submits a task to Claude Desktop or Cursor, the LLM executes an autonomous 4-phase Model Context Protocol loop:

Phase 1

Schema Introspection

Handshake lists all 10 tools and builds argument validators.

Phase 2

Argument Synthesis

Model extracts parameters from prompt and validates types against OpenAPI rules.

Phase 3

Stdio Execution

Bridge invokes live API with injected local credentials and captures raw HTTP response.

Phase 4

Output Remediation

LLM parses JSON results, handles status codes, and presents synthesized answers.

3. Multi-Client Installation Matrix & Setup Guides

Select your AI assistant below to view exact configuration file paths, JSON installation snippets, and launch commands.

Claude Desktop

claude_desktop_config.json
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Linux: ~/.config/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "avaza-com": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-openapi",
        "https://api.apis.guru/v2/specs/avaza.com/v1/swagger.json"
      ],
      "env": {
        "AVAZA_API_DOCUMENTATION_API_KEY": "your_avaza_api_documentation_api_key"
      }
    }
  }
}
Deep link

Cursor IDE

.cursor/mcp.json

Open Cursor Settings → Features → MCP Servers, or create .cursor/mcp.json in your project root.

{
  "mcpServers": {
    "avaza-com": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-openapi",
        "https://api.apis.guru/v2/specs/avaza.com/v1/swagger.json"
      ],
      "env": {
        "AVAZA_API_DOCUMENTATION_API_KEY": "your_avaza_api_documentation_api_key"
      }
    }
  }
}

Saves as .cursor/mcp.json in the download. Move it to your project root.

Deep link install →

VS Code / Cline Extension

cline_mcp_settings.json

Paste into your Cline extension MCP configuration or Roo Code host settings.

{
  "mcpServers": {
    "avaza-com": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-openapi",
        "https://api.apis.guru/v2/specs/avaza.com/v1/swagger.json"
      ],
      "env": {
        "AVAZA_API_DOCUMENTATION_API_KEY": "your_avaza_api_documentation_api_key"
      }
    }
  }
}

Zed Editor & Docker CLI

Zed / Docker

Docker container execution command:

docker run -i --rm -e AVAZA_API_DOCUMENTATION_API_KEY="YOUR_SECRET_VALUE" node:20-alpine npx -y @modelcontextprotocol/server-openapi https://api.apis.guru/v2/specs/avaza.com/v1/swagger.json

Zed settings context servers JSON:

{
  "context_servers": {
    "avaza-com": {
      "command": {
        "path": "npx",
        "args": [
          "-y",
          "@modelcontextprotocol/server-openapi",
          "https://api.apis.guru/v2/specs/avaza.com/v1/swagger.json"
        ],
        "env": {
          "AVAZA_API_DOCUMENTATION_API_KEY": "your_avaza_api_documentation_api_key"
        }
      }
    }
  }
}

Programmatic SDK Integration (TypeScript / Python)

Initialize the Avaza API Documentation MCP client directly in your backend codebase.

import { Client } from "@modelcontextprotocol/sdk/client/index.js";
import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js";

// Initialize Avaza API Documentation MCP client transport over stdio
const transport = new StdioClientTransport({
  command: "npx",
  args: ["-y","@modelcontextprotocol/server-openapi","https://api.apis.guru/v2/specs/avaza.com/v1/swagger.json"],
  env: { AVAZA_API_DOCUMENTATION_API_KEY: process.env.AVAZA_API_DOCUMENTATION_API_KEY || "YOUR_SECRET_KEY" }
});

const client = new Client(
  { name: "avaza-com-client", version: "1.0.0" },
  { capabilities: { tools: {}, resources: {}, prompts: {} } }
);

async function connectAndRun() {
  await client.connect(transport);
  const tools = await client.listTools();
  console.log("Connected to Avaza API Documentation MCP Server.");
  console.log("Discovered 10 mapped tools:", tools);
}

connectAndRun().catch(console.error);

Raw Stdio Schema Definition

schema.json

For standalone CLI wrappers, background daemon daemons, or custom script integrations:

{
  "mcpServers": {
    "avaza-com": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-openapi",
        "https://api.apis.guru/v2/specs/avaza.com/v1/swagger.json"
      ],
      "env": {
        "AVAZA_API_DOCUMENTATION_API_KEY": "your_avaza_api_documentation_api_key"
      }
    }
  }
}

4. Security, Authentication & Credential Management

Safely configure authentication tokens, isolate execution environments, and implement enterprise security best practices.

Required Environment Keys Reference

Variable NameRequiredTypeDefaultPurpose & Guidance
AVAZA_API_DOCUMENTATION_API_KEYREQUIREDSecret Key / TokenNone (Set in env)your_avaza_api_documentation_api_key

Zero-Downtime Token Rotation Protocol

  1. Generate Secondary Key: Create a new secret API token with identical scopes in your Avaza API Documentation developer portal.
  2. Update Client Configuration: Insert the new token inside the env block of your MCP client JSON config.
  3. Validate Connection: Issue a test query in Claude or Cursor to ensure handshake and tool calls succeed.
  4. Revoke Stale Token: Decommission the legacy key on the vendor portal to prevent unauthorized access.

Least-Privilege & Sandboxing Rules

  • Read-Only Token Scoping: Whenever your workflow only requires querying data, provision read-only credentials to prevent accidental mutations.
  • Local Process Isolation: Stdio transports run in isolated local subprocesses; secret credentials are never sent across the internet to MCP Bridge servers.
  • Prompt Injection Defense: AI model responses are sandboxed; verify generated destructive arguments before confirming execution in agent mode.

Enterprise Security Checklist (Mandatory Practices)

  • Never commit claude_desktop_config.json or .cursor/mcp.json containing raw secrets into public GitHub repositories.
  • Add .cursor/mcp.json and .env.local to your project's .gitignore file.
  • Always enforce TLS/HTTPS encryption on outbound network requests initiated by the server process.

5. Tool Parameter Schemas & Natural Language Execution

Mapped OpenAPI operations converted into discrete Model Context Protocol tools with strict JSON-RPC payload validators.

10 Total Tools Mapped
POST/ScheduleSeries/AddBooking
tools/call: avaza-com_post_ScheduleSeries_AddBooking

Create new Schedule Booking

Zero required query/path parameters for this endpoint.
JSON-RPC 2.0 Request Payload
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "avaza-com_post_ScheduleSeries_AddBooking",
    "arguments": {}
  }
}
Natural Language Prompt

"Use Avaza API Documentation to execute Create new Schedule Booking and output the formatted result."

POST/ScheduleSeries/AddLeave
tools/call: avaza-com_post_ScheduleSeries_AddLeave

Create new Leave Booking

Zero required query/path parameters for this endpoint.
JSON-RPC 2.0 Request Payload
{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "avaza-com_post_ScheduleSeries_AddLeave",
    "arguments": {}
  }
}
Natural Language Prompt

"Use Avaza API Documentation to execute Create new Leave Booking and output the formatted result."

PUT/ScheduleSeries/EditBooking
tools/call: avaza-com_put_ScheduleSeries_EditBooking

Edit Booking

Zero required query/path parameters for this endpoint.
JSON-RPC 2.0 Request Payload
{
  "jsonrpc": "2.0",
  "id": 3,
  "method": "tools/call",
  "params": {
    "name": "avaza-com_put_ScheduleSeries_EditBooking",
    "arguments": {}
  }
}
Natural Language Prompt

"Use Avaza API Documentation to execute Edit Booking and output the formatted result."

PUT/ScheduleSeries/EditLeave
tools/call: avaza-com_put_ScheduleSeries_EditLeave

Edit Leave Booking

Zero required query/path parameters for this endpoint.
JSON-RPC 2.0 Request Payload
{
  "jsonrpc": "2.0",
  "id": 4,
  "method": "tools/call",
  "params": {
    "name": "avaza-com_put_ScheduleSeries_EditLeave",
    "arguments": {}
  }
}
Natural Language Prompt

"Use Avaza API Documentation to execute Edit Leave Booking and output the formatted result."

GET/api/Account
tools/call: avaza-com_get_api_Account

Account Details

Zero required query/path parameters for this endpoint.
JSON-RPC 2.0 Request Payload
{
  "jsonrpc": "2.0",
  "id": 5,
  "method": "tools/call",
  "params": {
    "name": "avaza-com_get_api_Account",
    "arguments": {}
  }
}
Natural Language Prompt

"Use Avaza API Documentation to execute Account Details and output the formatted result."

GET/api/Bill
tools/call: avaza-com_get_api_Bill

Gets list of Bills

Zero required query/path parameters for this endpoint.
JSON-RPC 2.0 Request Payload
{
  "jsonrpc": "2.0",
  "id": 6,
  "method": "tools/call",
  "params": {
    "name": "avaza-com_get_api_Bill",
    "arguments": {}
  }
}
Natural Language Prompt

"Use Avaza API Documentation to execute Gets list of Bills and output the formatted result."

POST/api/Bill
tools/call: avaza-com_post_api_Bill

Create a new draft Bill

Zero required query/path parameters for this endpoint.
JSON-RPC 2.0 Request Payload
{
  "jsonrpc": "2.0",
  "id": 7,
  "method": "tools/call",
  "params": {
    "name": "avaza-com_post_api_Bill",
    "arguments": {}
  }
}
Natural Language Prompt

"Use Avaza API Documentation to execute Create a new draft Bill and output the formatted result."

GET/api/Bill/{id}
tools/call: avaza-com_get_api_Bill__id

Gets a Bill by Bill ID

Zero required query/path parameters for this endpoint.
JSON-RPC 2.0 Request Payload
{
  "jsonrpc": "2.0",
  "id": 8,
  "method": "tools/call",
  "params": {
    "name": "avaza-com_get_api_Bill__id",
    "arguments": {}
  }
}
Natural Language Prompt

"Use Avaza API Documentation to execute Gets a Bill by Bill ID and output the formatted result."

6. Interactive Troubleshooting & FAQ Accordion

Diagnose and resolve common JSON-RPC protocol error codes, connection disconnects, and schema refresh issues.

A 401 Unauthorized response indicates that the upstream Avaza API Documentation API rejected the authentication credential supplied in your MCP client's environment configuration. To resolve this: (1) Verify that your secret token is defined inside the "env" block of claude_desktop_config.json or .cursor/mcp.json rather than hardcoded in the command string. (2) Check whether Avaza API Documentation requires a prefix such as "Bearer <token>" in the authorization header. (3) Confirm that your API key has not expired and has been granted sufficient least-privilege scopes on the Avaza API Documentation developer dashboard.

If your MCP client fails to initialize tools for Avaza API Documentation: (1) Test the bridge launcher command ("npx -y @modelcontextprotocol/server-openapi https://api.apis.guru/v2/specs/avaza.com/v1/swagger.json") directly inside your terminal or shell to inspect stdout/stderr diagnostic traces. (2) Verify network connectivity to the schema source (https://api.apis.guru/v2/specs/avaza.com/v1/swagger.json). (3) Ensure Node.js (v18+) is installed and accessible in your system PATH. (4) For authenticated APIs, confirm credentials are configured in your client's "env" mapping rather than command arguments.

Similar Communication Configurations

Explore related API bridges with ready-to-use Model Context Protocol schemas.

Slack API

Communication

Send messages, manage channels, and integrate Slack notifications into your AI agent workflows.

https://mcpbridge.org/config/slack.json

Discord API

Communication

Send messages, manage servers, and integrate Discord bots into your AI agent workflows.

https://mcpbridge.org/config/discord.json

Twilio API

Communication

Send SMS, make calls, and manage communication channels through your AI agent.

https://mcpbridge.org/config/twilio.json

Email Activity (beta)

Communication

The Email Activity (beta) API, provided by [Your Email Service Provider], is a specialized suite of endpoints designed to grant programmatic access to granular email event data and system security configurations. Its core capability revolves around detailed filtering and search across two primary domains: user engagement events (like opens, clicks, and bounces) and security/access control settings. While the event data functionality is limited to a recent two-day window by default, it serves as a powerful tool for real-time monitoring and immediate post-campaign analysis. Typical use cases for enterprise teams include building internal dashboards for marketing performance, automating alerts for campaign anomalies (e.g., a sudden spike in bounces), and developing custom reporting pipelines that feed into business intelligence systems. The associated security endpoints—managing an access whitelist and configuring alert notifications—provide critical administrative control, allowing teams to programmatically define which IP addresses or systems can interact with their email infrastructure and to set up proactive monitoring for potential security or deliverability issues. When exposed as tools to an AI coding assistant via the Model Context Protocol (MCP), the value of the Email Activity API shifts from simple data retrieval to enabling intelligent, context-aware automation and synthesis. An AI agent, such as Claude Desktop or an IDE-integrated assistant, can leverage these endpoints not just to fetch data, but to perform complex reasoning on the results. For instance, instead of a developer manually querying for all "bounce" events, they can instruct the AI to "analyze the last 24 hours of bounce data, group them by recipient domain, and draft an alert for the ops team if the failure rate for our primary domain exceeds 1%." The AI can dynamically combine data from the activity endpoint with the current security whitelist via the `/access_settings/whitelist` endpoint to audit configurations, generating suggestions like "I noticed the marketing automation server's IP is not whitelisted, which may be causing the recent campaign sends to fail. Should I add it?" Practical workflow examples showcase the transformative potential of this integration. A developer can instruct the AI agent to perform dynamic tasks such as: "Query the `/alerts` endpoint, review the current conditions for our 'high bounce rate' alert, and suggest a more sensitive threshold based on the bounce data from the last hour, then propose the corresponding API call to update it." Alternatively, an agent could be tasked to "Audit our security posture by fetching the current whitelist, cross-reference it with recent access logs (if available through a separate log endpoint), and flag any IP addresses that have made numerous requests but are not currently whitelisted, recommending whether to create a new whitelist rule." This allows the AI to act as an operational analyst, continuously monitoring system state and suggesting or implementing administrative actions based on real-time data streams. Critical implementation considerations begin with the "None" authentication method indicated for this beta API, which is a significant security red flag. Developers must assume this is a placeholder or error and seek alternative, robust authentication (like OAuth 2.0 or API key via a secure header) as soon as the API matures. Until then, any integration must treat the endpoints as highly sensitive and be restricted to non-production, sandboxed environments only. When setting up the MCP server, adherence to the principle of least privilege is paramount: the API keys or tokens used should be scoped exclusively to the narrow set of email activity and security endpoints required for the specific workflow, with no unnecessary read/write permissions. Configuration should ensure all API calls are made over TLS, and any locally cached email event data must be treated as confidential, encrypted at rest and in transit to prevent exposure of sensitive user engagement information. Developers must also build in robust error handling for rate limits and the inherent instability of beta endpoints, designing their AI-driven workflows to gracefully manage changes in the API schema without failure.

https://mcpbridge.org/config/sendgrid-com.json