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DeepSeek-MCP

DeepSeek-MCP

by Abbas-F-R
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

review_architecture

Analyze system architecture to uncover design flaws, scalability issues, and coupling bottlenecks. Receive clear recommendations for architectural improvements.

Instructions

Analyze system architecture, design patterns, scalability, and coupling bottlenecks using Reasoner LLM

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": {
      "architecture_description": "<architecture_description>"
    },
    "name": "review_architecture"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelNoModel to use (default: deepseek-reasoner)
providerNoAI Provider
architecture_descriptionYesArchitecture specification or component layout

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description must disclose behavioral traits. It mentions 'using Reasoner LLM' which indicates the model, but does not explicitly state that the operation is read-only or describe the output format. The 'analyze' verb suggests non-destructive analysis, but this is not explicit.

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 a single concise sentence, front-loading the purpose and scope without extraneous information.

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 has no output schema and no annotations. The description covers the input scope and model, but does not indicate what the analysis output looks like or any limitations. Given the moderate complexity, this is a minor gap.

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 covers all three parameters with descriptions, so baseline is 3. The description mentions 'Reasoner LLM' which aligns with the model parameter, but adds no additional parameter semantics beyond the schema.

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 verb 'Analyze' plus specific resource 'system architecture, design patterns, scalability, and coupling bottlenecks' clearly states the tool's function. This distinct focus differentiates it from siblings like review_security or review_performance.

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 description implies usage for architecture-level analysis but does not explicitly state when to use this vs alternatives. It provides clear context ('Analyze system architecture...') but no exclusions or alternative recommendations.

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

Deploy Server

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