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jfdjaf

mcp-model-proxy

by jfdjaf

ask_model

Send prompts to Claude-compatible AI models through a local MCP server. Get text responses for queries using a standardized tool interface.

Instructions

Call a model via the local MCP server. Sends a prompt to an upstream service compatible with the Claude Messages API and returns the text response.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYes
systemNo
modelNo
maxTokensNo
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool sends a prompt and returns a text response, but lacks details on permissions, rate limits, error handling, or other behavioral traits like whether it's read-only or destructive. The description is minimal and does not compensate for the absence of annotations.

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 concise and front-loaded, consisting of two sentences that directly explain the tool's function without unnecessary details. Every sentence earns its place by defining the action and outcome efficiently.

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 complexity of a tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It does not explain return values, error cases, or provide sufficient context for safe and effective use, leaving significant gaps in understanding the tool's behavior and requirements.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate for the lack of parameter documentation. However, it does not mention any parameters or their semantics beyond implying a 'prompt' is sent. With 4 parameters (prompt, system, model, maxTokens) and no explanation in the description, it fails to add meaningful context beyond what the bare schema provides.

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 tool's purpose: 'Call a model via the local MCP server' specifies the action and target, and 'Sends a prompt to an upstream service compatible with the Claude Messages API and returns the text response' elaborates on the operation and outcome. It distinguishes the tool by mentioning the Claude Messages API compatibility, though there are no sibling tools to differentiate from.

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, prerequisites, or specific contexts. It mentions the Claude Messages API compatibility, which hints at usage with compatible services, but lacks explicit when/when-not instructions or named alternatives.

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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