Mirdan
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| FASTMCP_DEBUG | No | Enable verbose output for troubleshooting | false |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| enhance_promptA | Automatically enhance a coding prompt with quality requirements, codebase context, and tool recommendations. Args: prompt: The original developer prompt task_type: Override auto-detection (generation|refactor|debug|review|test|planning|auto) context_level: How much context to gather (minimal|auto|comprehensive) Returns: Enhanced prompt with quality requirements and tool recommendations |
| analyze_intentB | Analyze a prompt without enhancement, returning the detected intent, entities, and recommended approach. Args: prompt: The developer prompt to analyze Returns: Structured intent analysis |
| get_quality_standardsC | Retrieve quality standards for a language/framework combination. Args: language: Programming language (typescript, python, etc.) framework: Optional framework (react, fastapi, etc.) category: Filter to specific category (security|architecture|style|all) Returns: Quality standards for the specified language/framework |
| suggest_toolsA | Suggest which MCP tools should be used for a given intent. Args: intent_description: Description of what you're trying to do available_mcps: Comma-separated list of available MCPs (optional) discover_capabilities: If True, query actual MCP capabilities for recommended MCPs Returns: Tool recommendations with priorities and reasons |
| get_verification_checklistB | Get a verification checklist for a specific task type. Args: task_type: Type of task (generation|refactor|debug|review|test) touches_security: Whether the task involves security-sensitive code Returns: Verification checklist appropriate for the task |
| validate_code_qualityB | 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 |
| validate_plan_qualityA | Validate a plan for implementation by a less capable model. Returns a quality score and list of issues that need fixing. Args: plan: The plan text to validate target_model: Model that will implement (haiku|flash|cheap|capable) Cheaper models require stricter plan quality. Returns: Quality scores, issues list, and ready_for_cheap_model flag |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 7 tools
Each tool has a clearly distinct purpose with no overlap. analyze_intent detects intent, enhance_prompt improves prompts, get_quality_standards retrieves standards, get_verification_checklist provides checklists, suggest_tools recommends tools, validate_code_quality validates code, and validate_plan_quality validates plans. The boundaries are well-defined and unambiguous.
All tools follow a consistent verb_noun pattern with clear, descriptive names. The naming convention is uniform throughout: analyze_intent, enhance_prompt, get_quality_standards, get_verification_checklist, suggest_tools, validate_code_quality, and validate_plan_quality. There are no deviations or mixed styles.
With 7 tools, the count is well-scoped for the server's purpose of prompt analysis, enhancement, and quality validation. Each tool earns its place by covering distinct aspects of the workflow, from intent analysis to code and plan validation, without being excessive or insufficient.
The tool surface provides complete coverage for the domain of developer prompt and code quality management. It includes analysis, enhancement, standards retrieval, verification, tool suggestion, and validation for both code and plans, ensuring no dead ends and supporting a full lifecycle from prompt to implementation.