Skip to main content
Glama

list_providers

Retrieve a comprehensive list of all available LLM providers supported by the Just Prompt server for streamlined integration and access.

Instructions

List all available LLM providers

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Core handler function that iterates over ModelProviders enum to list all available providers with name, full_name, and short_name.
    def list_providers() -> List[Dict[str, str]]:
        """
        List all available providers with their full and short names.
        
        Returns:
            List of dictionaries with provider information
        """
        providers = []
        for provider in ModelProviders:
            providers.append({
                "name": provider.name,
                "full_name": provider.full_name,
                "short_name": provider.short_name
            })
        
        return providers
  • Pydantic input schema for the list_providers tool (no parameters required).
    class ListProvidersSchema(BaseModel):
        pass
  • Registration of the list_providers tool in the MCP server's list_tools() function.
    Tool(
        name=JustPromptTools.LIST_PROVIDERS,
        description="List all available LLM providers",
        inputSchema=ListProvidersSchema.schema(),
    ),
  • MCP tool dispatch handler that calls list_providers_func() and formats the output as TextContent for the MCP protocol.
    elif name == JustPromptTools.LIST_PROVIDERS:
        providers = list_providers_func()
        provider_text = "\nAvailable Providers:\n"
        for provider in providers:
            provider_text += f"- {provider['name']}: full_name='{provider['full_name']}', short_name='{provider['short_name']}'\n"
        return [TextContent(
            type="text",
            text=provider_text
        )]
  • Constant definition for the tool name in JustPromptTools enum.
    LIST_PROVIDERS = "list_providers"
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but provides no information about permissions needed, rate limits, pagination behavior, response format, or whether this is a read-only operation. For a tool with zero annotation coverage, this leaves significant behavioral gaps unaddressed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that communicates the core purpose without any wasted words. It's appropriately sized for a simple list operation and front-loads the essential information. Every word earns its place in this minimal description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has no annotations, no output schema, and the description provides only basic purpose information, there are significant completeness gaps. For even a simple list operation, the description should address response format, potential limitations, or behavioral context. The current description is insufficient for a tool that agents need to understand fully before invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters with 100% schema description coverage, so the schema already fully documents the parameter situation. The description appropriately doesn't discuss parameters since none exist. This earns a baseline score of 4 for parameter semantics when there are no parameters to document.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('List') and resource ('all available LLM providers'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'list_models', but the resource specificity (providers vs models) provides implicit differentiation. The description avoids tautology by not just restating the tool name.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'list_models' or other sibling tools. It doesn't mention prerequisites, context for usage, or any exclusions. While the purpose is clear, there's no explicit usage guidance beyond the basic action described.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Related Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/disler/just-prompt'

If you have feedback or need assistance with the MCP directory API, please join our Discord server