Skip to main content
Glama
Sign In
Mirdan

Mirdan

by S-Corkum
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

validate_code_quality

Validate code against quality standards including security, architecture, and style for multiple programming languages to ensure compliance with best practices.

Instructions

Validate generated code against quality standards.

Args: code: The code to validate language: Programming language (python|typescript|javascript|rust|go|auto) check_security: Validate against security standards check_architecture: Validate against architecture standards check_style: Validate against language-specific style standards severity_threshold: Minimum severity to include in results (error|warning|info)

Returns: Validation results with pass/fail, score, violations, and summary

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": "validate_code_quality"
  }
}

Input Schema

NameRequiredDescriptionDefault
codeYes
languageNoauto
check_securityNo
check_architectureNo
check_styleNo
severity_thresholdNowarning

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3/5.0
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. It mentions validation against standards but doesn't describe what happens during validation (e.g., external calls, processing time), error handling, or output format details beyond the basic return statement. This leaves significant gaps for a tool with 6 parameters.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized but not optimally structured. The initial sentence is clear, but the parameter and return sections are listed without integration into a cohesive narrative. While efficient, it could be more front-loaded with key usage context.

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

Completeness3/5

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

Given the tool's complexity (6 parameters, no annotations) and the presence of an output schema, the description is moderately complete. It covers parameter semantics well but lacks behavioral context and usage guidelines. The output schema likely details return values, reducing the need for that in the description, but overall gaps remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds substantial meaning beyond the input schema, which has 0% description coverage. It explains each parameter's purpose (e.g., 'code: The code to validate,' 'check_security: Validate against security standards'), including enum values for 'language' and 'severity_threshold.' This compensates well for the schema's lack of documentation.

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 as 'Validate generated code against quality standards,' which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'validate_plan_quality' or 'get_quality_standards,' leaving some ambiguity about when to choose this tool over alternatives.

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 its siblings. It doesn't mention alternatives like 'validate_plan_quality' for non-code validation or 'get_quality_standards' for retrieving standards, nor does it specify prerequisites or contextual constraints for usage.

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