Agent Server for Model Context Protocol (MCP)

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This is a specialized Model Context Protocol (MCP) backend service engineered to facilitate the inception, lifecycle management, and functional expansion of artificial intelligence agents. It operates through a sophisticated system for managing declarative instructions (prompts) and external capabilities (tools). The service achieves enhanced utility through native integration with the systemprompt.io ecosystem, specifically for streamlined creation, centralized governance, and rigorous version control of system prompts via the MCP standard.

A valid API key is mandatory for server utilization. Currently offered at no charge, though licensing terms are subject to future revisions. Secure your authentication token via this link.

This infrastructure leverages advanced Sampling and Notification mechanisms defined within the @modelcontextprotocol/sdk. Consequently, operational effectiveness is contingent upon employing advanced client applications that fully implement these features. A complimentary, open-source client, multimodal-mcp-client, is available to establish a complete, voice-enabled artificial intelligence operational pipeline.

Prerequisite Client Component

This server component is architecturally aligned for operation with the multimodal-mcp-client, which supplies the graphical user interface and voice interaction layer. Ensure both elements are deployed concurrently for maximal feature realization.

Value Proposition

• Entity Governance: Establish and oversee AI agents, configuring them with bespoke system instructions and utility sets.
• Modular Utility System: Introduce, revise, and amalgamate external functionalities to augment agent capabilities via MCP specifications.
• Instruction Repository: Centralized control over system prompts, complete with auditing trails for versions and associated metadata.
• Type-Safe Interoperability: Comprehensive TypeScript support ensuring robust type checking and reliable exception handling.
• MCP Conformance: Flawless operational harmony with the multimodal-mcp-client and other protocol-adherent frontends.
• Open Development: Freely accessible and modifiable under the terms of the MIT license.

Capabilities

Fundamental Operations

• MCP Protocol Adherence: Complete implementation of the Model Context Protocol for fluid interaction with AI entities.
• Vocalized Interaction Layer: Supports command reception via voice through the multimodal-mcp-client interface.
• Low-Latency Processing: Native support for response streaming and immediate, real-time conversational exchange.
• Strongly Typed Codebase: Full TypeScript utilization guaranteeing structural integrity and robust error mitigation.

Agent Lifecycle Management

• Provisioning and tailoring of AI agents for specific operational scopes.
• Administration of agent transactional states and contextual memory.
• Definition of agent behavioral patterns via system directives.
• Oversight and diagnosis of agent interaction flows.
• Configuration version control for agent blueprints.
• Management of underlying assets and resources pertaining to agents.

Advanced Utility Framework

Integrated utilities include:

• Prompt Administration
• create_prompt - Instantiates new system directives with descriptive data.
• edit_prompt - Updates extant prompt configurations, incorporating version increments.
• get_prompt - Retrieves precisely defined prompt specifications.
• Asset Governance
• create_resource - Provisions new configurations or persistent data elements for agents.
• edit_resource - Modifies existing persistent data elements.
• list_resources - Enumerates all accessible agent assets.
• read_resource - Fetches content from a designated agent asset.
• System Utilities
• systemprompt_heartbeat - Reports on the operational health and status of the service.
• systemprompt_fetch_resources - Pulls a registry of all available managed resources.
• Entity Control
• create_agent - Initializes a new systemprompt agent instance.
• edit_agent - Alters the parameters of an existing systemprompt agent.
• list_agents - Generates a catalog of deployed systemprompt agents.

Sampling & Event Signaling

• Sophisticated parameter controls for AI response generation sampling.
• A reactive notification mechanism for signaling critical agent events.
• Adjustable configuration knobs for sampling behavior.
• An event-driven architectural pattern underpinning all asynchronous signaling.

Interfacing Features

• Authentication handling via API key verification.
• Tracking of user subscription tiers and billing statuses.
• Mechanisms for subscription level administration.
• Comprehensive monitoring and performance analytics capture.

Development & Testing Utilities

• Integrated diagnostic tools for runtime inspection.
• Utility sets and mock data fixtures for iterative testing.
• Type-safe apparatus for creating simulated environments.
• A comprehensive framework dedicated to quality assurance.

🎥 Demonstration & Showcase

View the system's capabilities in action via our video presentation:

▶️ View Demonstration Video

The demonstration covers:

• Conversational control driven by vocal commands.
• Processing of combined sensory inputs (multimodal).
• Automated execution of defined utility sequences.
• Synthesis of voice output in real-time.

Engineering Workflow

Dependency installation:

bash npm install

Compiling the server binaries:

bash npm run build

For continuous development with automatic recompilation:

bash npm run watch

Deployment Instructions

Installation via Smithery

To deploy the SystemPrompt Agent automatically for use with Claude Desktop utilizing Smithery:

bash npx -y @smithery/cli install systemprompt-agent-server --client claude

Manual Configuration

To manually configure integration with Claude Desktop, incorporate the following server configuration block into your settings file:

On MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

{ "mcpServers": { "systemprompt-agent-server": { "command": "/path/to/systemprompt-agent-server/build/index.js" } } }

Diagnostics

Troubleshooting communication across stdio channels in MCP servers can be complex. We advise utilizing the MCP Inspector, accessible via a dedicated package script:

bash npm run inspector

This command will yield a network address to access browser-based debugging interfaces.

Quality Assurance

This repository employs the Jest testing framework, configured for TypeScript and native ESM (ECMAScript Modules) operation.

Test Environment Setup

The testing configuration incorporates:

• Full TypeScript compilation supporting ESM modules.
• Automated global mocking of the fetch API.
• Automatic state cleanup following each test execution.
• Custom validation assertions for enhanced result checking.
• Specialized utilities for creating type-safe test mocks.

Module Resolution Strategy

The project employs a bifurcated approach to module loading:

• Application source code utilizes ESM, requiring explicit .js extensions in imports.
• Test scripts leverage CommonJS compatibility for Jest execution.

This duality is managed via distinct TypeScript configuration files:

• tsconfig.json: Primary configuration targeting source code output (ESM).
• tsconfig.test.json: Specialized configuration tailored for the testing environment (CommonJS).

typescript // Application source code imports (ESM) import { Something } from "../path/to/module.js";

// Test file imports (CommonJS compatible) import { Something } from "../path/to/module";

Executing Tests

bash

Run the complete test suite

npm test

Execute tests in continuous watch mode

npm run test:watch

Generate a detailed coverage report

npm run test:coverage

Test Placement

Test files are strategically situated within __tests__ subdirectories, adjacent to the source modules they validate, following the naming pattern *.test.ts.

Associated References

• Multimodal MCP Client - The client component specialized for voice interaction.
• systemprompt.io Technical Documentation

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