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DeepSeek-MCP

DeepSeek-MCP

by Abbas-F-R
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

documentation

Generate clean technical Markdown documentation for APIs, classes, or modules. Transform source code into structured, readable docs.

Instructions

Generate clean technical Markdown documentation for APIs, classes, or modules

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": {
      "code": "<code>"
    },
    "name": "documentation"
  }
}

Input Schema

NameRequiredDescriptionDefault
codeYesCode to document
modelNoModel to use (default: deepseek-chat)
doc_typeNoType of documentation (e.g. API Docs, README, JSDoc/Docstring)
providerNoAI Provider

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description must carry the full burden of behavioral transparency. It only states that it generates Markdown documentation, leaving unspecified whether it writes to files, returns a string, requires network access, or modifies code. This is a significant gap without annotations to fall back on.

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 that immediately conveys the core function. There is no fluff, and it is front-loaded with the verb and resource. It is appropriately sized for the amount of information it provides.

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?

With no output schema and no annotations, the description must explain return values and behavioral context, but it does not. It fails to mention what the tool actually returns (e.g., Markdown string, file path), how doc_type changes output, or any limitations. Given the presence of sibling tools, the description is incomplete for safe and effective use.

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 schema already documents all four parameters. The description does not add any extra meaning beyond that, such as how 'doc_type' influences the output or how 'model' and 'provider' are used. Baseline of 3 is appropriate when schema covers everything.

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: generating clean technical Markdown documentation for APIs, classes, or modules. However, it does not distinguish itself from the sibling tool 'generate_documentation', which likely serves a nearly identical purpose.

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 like 'generate_documentation' or other generate_* tools. It only implies use for documentation generation but does not specify contexts, exclusions, or 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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