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kamelirzouni

Deepseek R1 MCP Server

by kamelirzouni

deepseek_r1

Generate text responses for reasoning tasks using the DeepSeek R1 language model with configurable parameters for token limits and temperature.

Instructions

Generate text using DeepSeek R1 model

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesInput text for DeepSeek
max_tokensNoMaximum tokens to generate (default: 8192)
temperatureNoSampling temperature (default: 0.2)
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. While 'Generate text' implies a read/write operation that creates content, it lacks critical behavioral details such as rate limits, authentication requirements, response format, error conditions, or whether it's idempotent. The description adds minimal value beyond the basic function.

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 extremely concise with a single, clear sentence that directly states the tool's purpose. There's no wasted language or unnecessary elaboration, making it efficiently front-loaded and easy to parse.

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?

For a text generation tool with 3 parameters and no output schema, the description is insufficiently complete. It doesn't explain what kind of text is generated, typical use cases, limitations, or what the return value looks like. The combination of no annotations and no output schema means the description should provide more contextual information than it does.

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?

The schema description coverage is 100%, meaning all parameters are well-documented in the schema itself. The description adds no additional parameter information beyond what's already in the schema, so it meets the baseline expectation but doesn't provide extra semantic context.

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 action ('Generate text') and specifies the resource ('using DeepSeek R1 model'), which provides a specific verb+resource combination. However, since there are no sibling tools mentioned, there's no opportunity to distinguish from alternatives, preventing a perfect score of 5.

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 contextual constraints. It simply states what the tool does without any usage instructions, which is insufficient for effective tool selection.

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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