Skip to main content
Glama
Sign In
deepseek-mcp

deepseek-mcp

by Korck-lab
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Local

chat

Send an OpenAI-style message list to DeepSeek and receive the assistant's reply text.

Instructions

Send a chat completion request to DeepSeek. Accepts an OpenAI-style message list and returns the assistant's reply text.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "messages": "<messages>"
    },
    "name": "chat"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelNoDeepSeek model id (default: deepseek-chat; alternative: deepseek-reasoner)
messagesYesChat messages, newest last. Each: {role: system|user|assistant, content: string}
max_tokensNoMax output tokens (default: 2048)
temperatureNoSampling temperature (0-2)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4.2/5.0
Behavior4/5

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

No annotations provided, so the description carries the full burden. It discloses the core behavior (sends request, returns reply text) and the message format. It doesn't mention side effects like API costs or rate limits, but for a stateless generation tool this is adequate.

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?

Two sentences, front-loaded with the main action and result. No redundancy or wasted words.

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

Completeness4/5

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

The tool is simple with well-documented params. It lacks an output schema, but the description states the return type ('assistant's reply text'), which suffices. No obvious missing context given its limited scope.

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 100%, so the baseline is 3. The description adds only a minor nuance ('OpenAI-style' message list) beyond the schema, but does not meaningfully explain parameters further.

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 states a specific action ('Send a chat completion request to DeepSeek') and clearly distinguishes this tool from siblings like list_models and list_harnesses. Mentioning OpenAI-style messages and the reply text further clarifies its unique role.

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

Usage Guidelines4/5

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

The context is clear: this tool is for generating chat completions, while siblings manage models/harnesses. However, there is no explicit 'when not to use' or alternative comparison, so it falls short of a 5.

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

Deploy Server

Other Tools