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itachiuchihadev

Multi-Provider LLM MCP Server

query_llm

Send prompts to multiple LLM providers (Gemini, OpenAI, Anthropic) and retrieve model responses. Supports chat history, system prompts, and response formatting.

Instructions

Query a supported LLM provider (Gemini, OpenAI, Anthropic, Cohere, Groq, Mistral, OpenRouter) with a prompt and retrieve the model response.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoOptional specific model ID (e.g., gpt-4o-mini, gemini-1.5-flash). Defaults to a recommended model if omitted.
apiKeyNoOptional API Key for authentication. If omitted, falls back to environment variables (e.g. GEMINI_API_KEY, OPENAI_API_KEY).
promptYesThe user prompt or instruction message.
providerYesThe LLM API provider to call.
maxTokensNoOptional maximum number of output tokens to generate.
chatHistoryNoOptional array of message history objects for context.
temperatureNoOptional sampling temperature (0.0 to 2.0).
systemPromptNoOptional system instructions to direct behavior/formatting.
responseFormatNoOptional response structure: "text" (default raw response), "json_object" (forces valid JSON outputs), or "detailed" (returns markdown detailed report including token usage).
Behavior2/5

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

No annotations are provided, so the description must cover behavioral traits. It only states it queries and retrieves a response, omitting details about external API calls, potential costs, latency, or authentication fallback behavior.

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 a single, clear sentence with no wasted words. It is appropriately front-loaded but could be slightly expanded without harming conciseness.

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's 9 parameters, no output schema, and no annotations, the description is insufficient. It does not mention chat history, temperature, system prompt, or response format, leaving the agent without context for proper usage.

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 coverage is 100%, so baseline is 3. The description does not add extra meaning beyond the schema; it merely mentions a prompt without elaborating on optional parameters or defaults.

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 tool queries an LLM provider with a prompt and retrieves a response. It lists specific providers, distinguishing it from the sibling tool list_providers.

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. It does not mention when not to use it or any prerequisites, such as needing an API key or choosing a provider.

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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