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davehenke

rekordbox-mcp

validate_track_ids

Check track ID validity in rekordbox databases to identify which IDs work and which don't for reliable DJ data access.

Instructions

Validate a list of track IDs and show which are valid/invalid.

Args: track_ids: List of track IDs to validate

Returns: Validation results with valid and invalid IDs

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
track_idsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • MCP tool handler and registration for validate_track_ids. Checks database connection and delegates to RekordboxDatabase.validate_track_ids method.
    @mcp.tool()
    async def validate_track_ids(track_ids: List[str]) -> Dict[str, Any]:
        """
        Validate a list of track IDs and show which are valid/invalid.
        
        Args:
            track_ids: List of track IDs to validate
            
        Returns:
            Validation results with valid and invalid IDs
        """
        if not db:
            raise RuntimeError("Database not initialized.")
        
        validation = await db.validate_track_ids(track_ids)
        return validation
  • Core implementation of track ID validation in RekordboxDatabase class. Loads all active tracks, builds set of existing IDs, and categorizes input IDs as valid or invalid.
    async def validate_track_ids(self, track_ids: List[str]) -> Dict[str, Any]:
        """Validate track IDs."""
        if not self.db:
            raise RuntimeError("Database not connected")
        
        all_content = list(self.db.get_content())
        active_content = [c for c in all_content if getattr(c, 'rb_local_deleted', 0) == 0]
        existing_ids = {str(content.ID) for content in active_content}
        
        valid = []
        invalid = []
        
        for track_id in track_ids:
            if track_id in existing_ids:
                valid.append(track_id)
            else:
                invalid.append(track_id)
        
        return {
            "valid": valid,
            "invalid": invalid,
            "total_checked": len(track_ids),
            "valid_count": len(valid),
            "invalid_count": len(invalid)
        }
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It mentions the tool validates IDs and returns results, but lacks details on behavioral traits like what constitutes a 'valid' ID (e.g., format, existence in a database), error handling for invalid inputs, or performance aspects like rate limits. The description is minimal and does not compensate for the absence of annotations.

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

Conciseness4/5

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

The description is front-loaded with the core purpose in the first sentence, followed by structured sections for Args and Returns. It is concise with no wasted words, though the lack of detailed guidance or behavioral context means it could be more informative without sacrificing brevity.

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 moderate complexity (single parameter, validation function) and the presence of an output schema (which handles return values), the description is minimally adequate. However, with no annotations and low schema coverage, it lacks completeness in explaining validation criteria, error cases, or integration with sibling tools, leaving room for improvement.

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 0%, so the description must compensate. It adds basic semantics by explaining that 'track_ids' is a 'List of track IDs to validate', which clarifies the parameter's purpose beyond the schema's array of strings. However, it does not provide details on ID format, constraints, or examples, leaving gaps in understanding.

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: 'Validate a list of track IDs and show which are valid/invalid.' It specifies the verb (validate) and resource (track IDs), but does not explicitly differentiate it from sibling tools like 'get_track_details' or 'search_tracks', which might also involve track ID validation indirectly.

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. It does not mention prerequisites, such as needing track IDs from other tools, or compare it to siblings like 'get_track_details' that might validate IDs as part of their operation. Usage is implied only by the purpose statement.

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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