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DeepSeek-MCP

DeepSeek-MCP

by Abbas-F-R
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TypeScript
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write_tests

Generate high-coverage unit or integration tests for provided code. Choose framework and AI model to ensure reliable validation.

Instructions

Generate high-coverage unit or integration tests for the provided code

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": "write_tests"
  }
}

Input Schema

NameRequiredDescriptionDefault
codeYesTarget code for test generation
modelNoModel to use (default: deepseek-chat)
providerNoAI Provider
frameworkNoTest framework (e.g. Jest, Vitest, JUnit, xUnit, PyTest)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations, the description carries the full burden for behavioral disclosure. It only states the intended outcome ('generate tests') without mentioning side effects, output format, file writes, or required permissions.

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, front-loaded sentence with no redundant words. It states the core purpose immediately.

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?

The description is too sparse for a tool with no output schema and no annotations. It fails to specify whether tests are returned as text, written to files, or what the output format is, making it incomplete for confident 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 baseline is a 3. The description adds minimal extra meaning (e.g., 'high-coverage', 'unit or integration') but does not elaborate on parameter usage beyond the schema.

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 generates tests for provided code, with a specific verb and resource. However, it does not differentiate from the sibling tool 'generate_tests', which appears to serve a similar purpose.

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

Usage Guidelines3/5

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

The description implies usage: given code, generate tests. It lacks explicit guidance on when to use this over alternatives like 'generate_tests', and provides no exclusions or conditions.

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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