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Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
PYTHONPATHNoPython path for module resolutionsrc
VLLM_MCP_HOSTNoServer host (optional)localhost
VLLM_MCP_PORTNoServer port (optional)8080
OPENAI_API_KEYNoYour OpenAI API key
OPENAI_BASE_URLNoOpenAI base URL (optional)https://api.openai.com/v1
DASHSCOPE_API_KEYYesYour Dashscope API key
VLLM_MCP_LOG_LEVELNoLog level (optional)INFO
VLLM_MCP_TRANSPORTNoTransport type (optional)stdio
OPENAI_DEFAULT_MODELNoDefault OpenAI model to usegpt-4o
DASHSCOPE_DEFAULT_MODELNoDefault Dashscope model to useqwen-vl-plus
OPENAI_SUPPORTED_MODELSNoComma-separated list of supported OpenAI modelsgpt-4o,gpt-4o-mini,gpt-4-turbo,gpt-4-vision-preview
DASHSCOPE_SUPPORTED_MODELSNoComma-separated list of supported Dashscope modelsqwen-vl-plus,qwen-vl-max,qwen-vl-chat,qwen2-vl-7b-instruct,qwen2-vl-72b-instruct

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Server capabilities have not been inspected yet.

Tools

Functions exposed to the LLM to take actions

NameDescription
generate_multimodal_responseC

Generate response from multimodal model.

        Args:
            model: Model name to use
            prompt: Text prompt
            image_urls: Optional list of image URLs
            file_paths: Optional list of file paths
            system_prompt: Optional system prompt
            max_tokens: Maximum tokens to generate
            temperature: Generation temperature
            provider: Optional provider name (openai, dashscope)

        Returns:
            Generated response text
        
list_available_providersB

List available model providers and their configurations.

        Returns:
            JSON string of available providers and their models
        
validate_multimodal_requestB

Validate if a multimodal request is supported.

        Args:
            model: Model name to validate
            image_count: Number of images in request
            file_count: Number of files in request
            provider: Optional provider name

        Returns:
            Validation result
        

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

TDQS

B3.4/5.0

Scored across 3 tools

Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap. generate_multimodal_response handles actual generation, list_available_providers provides configuration information, and validate_multimodal_request performs validation checks. An agent can easily distinguish between these three functions.

Naming Consistency5/5

All three tools follow a consistent verb_noun naming pattern (generate_multimodal_response, list_available_providers, validate_multimodal_request). The naming is uniform, predictable, and clearly communicates each tool's function without any style mixing or deviations.

Tool Count3/5

With only 3 tools, this server feels somewhat thin for a multimodal generation service. While the tools cover core functionality, typical MCP servers in this domain would include additional operations like model management, conversation history, or specialized generation modes. The count is borderline but functional.

Completeness4/5

The tool set covers the essential workflow: checking available providers, validating requests, and generating responses. However, there are minor gaps such as no tool for managing conversation context, handling streaming responses, or providing model-specific configuration options that would enhance the agent's ability to work effectively with multimodal models.

Maintenance

ActivityInactive
ResponsivenessNo issues