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sjquant
by sjquant

run_llm

Submit a text prompt to an LLM and receive its response. Supports multiple AI providers with adjustable model, temperature, and token limits.

Instructions

Run a prompt through an LLM and return the response.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYes
model_nameNoSpecific model name. Available models: anthropic:claude-3-7-sonnet-latest, anthropic:claude-3-5-haiku-latest, anthropic:claude-3-5-sonnet-latest, anthropic:claude-3-opus-latest, claude-3-7-sonnet-latest, claude-3-5-haiku-latest, bedrock:amazon.titan-tg1-large, bedrock:amazon.titan-text-lite-v1, bedrock:amazon.titan-text-express-v1, bedrock:us.amazon.nova-pro-v1:0, bedrock:us.amazon.nova-lite-v1:0, bedrock:us.amazon.nova-micro-v1:0, bedrock:anthropic.claude-3-5-sonnet-20241022-v2:0, bedrock:us.anthropic.claude-3-5-sonnet-20241022-v2:0, bedrock:anthropic.claude-3-5-haiku-20241022-v1:0, bedrock:us.anthropic.claude-3-5-haiku-20241022-v1:0, bedrock:anthropic.claude-instant-v1, bedrock:anthropic.claude-v2:1, bedrock:anthropic.claude-v2, bedrock:anthropic.claude-3-sonnet-20240229-v1:0, bedrock:us.anthropic.claude-3-sonnet-20240229-v1:0, bedrock:anthropic.claude-3-haiku-20240307-v1:0, bedrock:us.anthropic.claude-3-haiku-20240307-v1:0, bedrock:anthropic.claude-3-opus-20240229-v1:0, bedrock:us.anthropic.claude-3-opus-20240229-v1:0, bedrock:anthropic.claude-3-5-sonnet-20240620-v1:0, bedrock:us.anthropic.claude-3-5-sonnet-20240620-v1:0, bedrock:anthropic.claude-3-7-sonnet-20250219-v1:0, bedrock:us.anthropic.claude-3-7-sonnet-20250219-v1:0, bedrock:cohere.command-text-v14, bedrock:cohere.command-r-v1:0, bedrock:cohere.command-r-plus-v1:0, bedrock:cohere.command-light-text-v14, bedrock:meta.llama3-8b-instruct-v1:0, bedrock:meta.llama3-70b-instruct-v1:0, bedrock:meta.llama3-1-8b-instruct-v1:0, bedrock:us.meta.llama3-1-8b-instruct-v1:0, bedrock:meta.llama3-1-70b-instruct-v1:0, bedrock:us.meta.llama3-1-70b-instruct-v1:0, bedrock:meta.llama3-1-405b-instruct-v1:0, bedrock:us.meta.llama3-2-11b-instruct-v1:0, bedrock:us.meta.llama3-2-90b-instruct-v1:0, bedrock:us.meta.llama3-2-1b-instruct-v1:0, bedrock:us.meta.llama3-2-3b-instruct-v1:0, bedrock:us.meta.llama3-3-70b-instruct-v1:0, bedrock:mistral.mistral-7b-instruct-v0:2, bedrock:mistral.mixtral-8x7b-instruct-v0:1, bedrock:mistral.mistral-large-2402-v1:0, bedrock:mistral.mistral-large-2407-v1:0, claude-3-5-sonnet-latest, claude-3-opus-latest, cohere:c4ai-aya-expanse-32b, cohere:c4ai-aya-expanse-8b, cohere:command, cohere:command-light, cohere:command-light-nightly, cohere:command-nightly, cohere:command-r, cohere:command-r-03-2024, cohere:command-r-08-2024, cohere:command-r-plus, cohere:command-r-plus-04-2024, cohere:command-r-plus-08-2024, cohere:command-r7b-12-2024, deepseek:deepseek-chat, deepseek:deepseek-reasoner, google-gla:gemini-1.0-pro, google-gla:gemini-1.5-flash, google-gla:gemini-1.5-flash-8b, google-gla:gemini-1.5-pro, google-gla:gemini-2.0-flash-exp, google-gla:gemini-2.0-flash-thinking-exp-01-21, google-gla:gemini-exp-1206, google-gla:gemini-2.0-flash, google-gla:gemini-2.0-flash-lite-preview-02-05, google-gla:gemini-2.0-pro-exp-02-05, google-vertex:gemini-1.0-pro, google-vertex:gemini-1.5-flash, google-vertex:gemini-1.5-flash-8b, google-vertex:gemini-1.5-pro, google-vertex:gemini-2.0-flash-exp, google-vertex:gemini-2.0-flash-thinking-exp-01-21, google-vertex:gemini-exp-1206, google-vertex:gemini-2.0-flash, google-vertex:gemini-2.0-flash-lite-preview-02-05, google-vertex:gemini-2.0-pro-exp-02-05, gpt-3.5-turbo, gpt-3.5-turbo-0125, gpt-3.5-turbo-0301, gpt-3.5-turbo-0613, gpt-3.5-turbo-1106, gpt-3.5-turbo-16k, gpt-3.5-turbo-16k-0613, gpt-4, gpt-4-0125-preview, gpt-4-0314, gpt-4-0613, gpt-4-1106-preview, gpt-4-32k, gpt-4-32k-0314, gpt-4-32k-0613, gpt-4-turbo, gpt-4-turbo-2024-04-09, gpt-4-turbo-preview, gpt-4-vision-preview, gpt-4.5-preview, gpt-4.5-preview-2025-02-27, gpt-4o, gpt-4o-2024-05-13, gpt-4o-2024-08-06, gpt-4o-2024-11-20, gpt-4o-audio-preview, gpt-4o-audio-preview-2024-10-01, gpt-4o-audio-preview-2024-12-17, gpt-4o-mini, gpt-4o-mini-2024-07-18, gpt-4o-mini-audio-preview, gpt-4o-mini-audio-preview-2024-12-17, groq:gemma2-9b-it, groq:llama-3.1-8b-instant, groq:llama-3.2-11b-vision-preview, groq:llama-3.2-1b-preview, groq:llama-3.2-3b-preview, groq:llama-3.2-90b-vision-preview, groq:llama-3.3-70b-specdec, groq:llama-3.3-70b-versatile, groq:llama3-70b-8192, groq:llama3-8b-8192, groq:mixtral-8x7b-32768, mistral:codestral-latest, mistral:mistral-large-latest, mistral:mistral-moderation-latest, mistral:mistral-small-latest, o1, o1-2024-12-17, o1-mini, o1-mini-2024-09-12, o1-preview, o1-preview-2024-09-12, o3-mini, o3-mini-2025-01-31, openai:chatgpt-4o-latest, openai:gpt-3.5-turbo, openai:gpt-3.5-turbo-0125, openai:gpt-3.5-turbo-0301, openai:gpt-3.5-turbo-0613, openai:gpt-3.5-turbo-1106, openai:gpt-3.5-turbo-16k, openai:gpt-3.5-turbo-16k-0613, openai:gpt-4, openai:gpt-4-0125-preview, openai:gpt-4-0314, openai:gpt-4-0613, openai:gpt-4-1106-preview, openai:gpt-4-32k, openai:gpt-4-32k-0314, openai:gpt-4-32k-0613, openai:gpt-4-turbo, openai:gpt-4-turbo-2024-04-09, openai:gpt-4-turbo-preview, openai:gpt-4-vision-preview, openai:gpt-4.5-preview, openai:gpt-4.5-preview-2025-02-27, openai:gpt-4o, openai:gpt-4o-2024-05-13, openai:gpt-4o-2024-08-06, openai:gpt-4o-2024-11-20, openai:gpt-4o-audio-preview, openai:gpt-4o-audio-preview-2024-10-01, openai:gpt-4o-audio-preview-2024-12-17, openai:gpt-4o-mini, openai:gpt-4o-mini-2024-07-18, openai:gpt-4o-mini-audio-preview, openai:gpt-4o-mini-audio-preview-2024-12-17, openai:o1, openai:o1-2024-12-17, openai:o1-mini, openai:o1-mini-2024-09-12, openai:o1-preview, openai:o1-preview-2024-09-12, openai:o3-mini, openai:o3-mini-2025-01-31, testopenai:gpt-4o-mini
temperatureNoControls randomness (0.0 to 1.0)
max_tokensNoMaximum number of tokens to generate
system_promptNoOptional system prompt to guide the model's behavior

Implementation Reference

  • The async function 'run_llm' that executes the LLM tool logic. It uses Pydantic AI's Agent to run a prompt with the specified model, temperature, max_tokens, and optional system prompt, then returns the response as JSON.
    @mcp.tool()
    async def run_llm(
        prompt: str,
        model_name: KnownModelName = Field(
            default="openai:gpt-4o-mini",
            description=f"Specific model name. Available models: {', '.join(KnownModelName.__args__)}",
        ),
        temperature: float = Field(
            default=0.7,
            description="Controls randomness (0.0 to 1.0)",
        ),
        max_tokens: int = Field(
            default=8192,
            description="Maximum number of tokens to generate",
        ),
        system_prompt: str = Field(
            default="",
            description="Optional system prompt to guide the model's behavior",
        ),
    ) -> str:
  • The '@mcp.tool()' decorator registers 'run_llm' as an MCP tool on the FastMCP instance.
    @mcp.tool()
  • The 'LLMResponse' Pydantic model defines the output schema for the run_llm tool, containing content, model_name, usage, and temperature fields.
    class LLMResponse(BaseModel):
        """Response from an LLM."""
    
        content: str
        model_name: str
        usage: Usage
        temperature: float
  • The function parameters (prompt, model_name, temperature, max_tokens, system_prompt) serve as the input schema, with Field for descriptions and defaults, using Pydantic AI's KnownModelName type.
    async def run_llm(
        prompt: str,
        model_name: KnownModelName = Field(
            default="openai:gpt-4o-mini",
            description=f"Specific model name. Available models: {', '.join(KnownModelName.__args__)}",
        ),
        temperature: float = Field(
            default=0.7,
            description="Controls randomness (0.0 to 1.0)",
        ),
        max_tokens: int = Field(
            default=8192,
            description="Maximum number of tokens to generate",
        ),
        system_prompt: str = Field(
            default="",
            description="Optional system prompt to guide the model's behavior",
        ),
Behavior2/5

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

No annotations are provided, so the description carries full burden for behavioral transparency. It fails to mention important traits like streaming, cost, latency, or error handling, leaving the agent uninformed.

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

Conciseness4/5

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

The description is one concise sentence, front-loading the action. It is not verbose, but it omits critical details, so it is not a perfect 5.

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?

With 5 parameters, no output schema, and no annotations, the description is incomplete. It does not describe return values, optional parameters, or implications of choices like model or temperature.

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

Parameters3/5

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

Schema description coverage is 80%, so the burden on the description is lower. However, the description adds no value beyond the schema—it does not explain any parameter semantics, so a baseline of 3 is appropriate.

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

Purpose5/5

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

The description clearly states the action ('run a prompt') and the resource ('LLM'), with the purpose of getting a response. It is specific and not a tautology.

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?

No guidance is provided on when to use this tool versus alternatives. Since there are no sibling tools, the lack is less critical, but the description still offers no usage context or preconditions.

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

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