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MCP Kafka Schema Reg

count_schema_versions

Track and count schema versions in MCP Kafka Schema Registry by specifying the subject. Use this tool to manage and monitor schema evolution efficiently.

Instructions

Count the number of versions for a specific schema.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contextNo
registryNo
subjectYes

Implementation Reference

  • Core implementation of the count_schema_versions tool handler. Fetches schema versions using get_schema_versions helper, computes count, and returns structured response with metadata.
    @structured_output("count_schema_versions", fallback_on_error=True)
    def count_schema_versions_tool(
        subject: str,
        registry_manager,
        registry_mode: str,
        context: Optional[str] = None,
        registry: Optional[str] = None,
    ) -> Dict[str, Any]:
        """
        Count the number of versions for a specific schema.
    
        Args:
            subject: The subject name
            context: Optional schema context
            registry: Optional registry name (ignored in single-registry mode)
    
        Returns:
            Dictionary containing version count and details with registry metadata and structured validation
        """
        try:
            if registry_mode == "single":
                client = get_default_client(registry_manager)
            else:
                client = registry_manager.get_registry(registry)
                if client is None:
                    return create_error_response(
                        f"Registry '{registry}' not found",
                        error_code="REGISTRY_NOT_FOUND",
                        registry_mode=registry_mode,
                    )
    
            # Import the function here to avoid circular imports
            from kafka_schema_registry_unified_mcp import get_schema_versions
    
            versions = get_schema_versions(subject, context, registry)
            if isinstance(versions, dict) and "error" in versions:
                return create_error_response(
                    f"Failed to get schema versions: {versions.get('error')}",
                    error_code="SCHEMA_VERSIONS_RETRIEVAL_FAILED",
                    registry_mode=registry_mode,
                )
    
            # Get registry metadata
            metadata = client.get_server_metadata()
    
            result = {
                "registry": (client.config.name if hasattr(client.config, "name") else "default"),
                "context": context or "default",
                "subject": subject,
                "count": len(versions),
                "scope": "versions",
                "versions": versions,
                "counted_at": datetime.now().isoformat(),
                "registry_mode": registry_mode,
                "mcp_protocol_version": "2025-06-18",
            }
    
            # Add metadata information, but preserve the scope field
            metadata_copy = metadata.copy()
            if "scope" in metadata_copy:
                # Preserve the simple string scope, but add server scope info separately
                metadata_copy["server_scope"] = metadata_copy.pop("scope")
            result.update(metadata_copy)
    
            return result
        except Exception as e:
            return create_error_response(str(e), error_code="VERSION_COUNT_FAILED", registry_mode=registry_mode)
  • JSON Schema definition (COUNT_SCHEMA) used for output validation of the count_schema_versions tool and other count tools.
    COUNT_SCHEMA = {
        "type": "object",
        "properties": {
            "count": {"type": "integer", "minimum": 0, "description": "Count result"},
            "scope": {
                "type": "string",
                "description": "What was counted (contexts, schemas, versions)",
            },
            "context": {
                "type": "string",
                "description": "Context name if scoped to context",
            },
            "registry": {"type": "string", "description": "Registry name"},
            **METADATA_FIELDS,
        },
        "required": ["count", "scope"],
        "additionalProperties": True,
    }
  • Mapping of 'count_schema_versions' tool name to its output schema (COUNT_SCHEMA) in TOOL_OUTPUT_SCHEMAS dictionary.
    "count_schema_versions": COUNT_SCHEMA,
  • Registration of operation metadata for count_schema_versions in task manager's OPERATION_METADATA, classifying it as QUICK DIRECT execution.
    "count_schema_versions": {
        "duration": OperationDuration.QUICK,
        "pattern": AsyncPattern.DIRECT,
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 of behavioral disclosure. It states the action but doesn't describe what 'count' entails (e.g., returns an integer, may require permissions, or has rate limits). For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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, clear sentence with no wasted words, making it highly concise and front-loaded. Every part of the sentence directly contributes to explaining the tool's purpose.

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?

Given no annotations, 0% schema coverage, and no output schema, the description is insufficient. It doesn't cover parameter meanings, return values, or behavioral traits, leaving the agent with inadequate information to use the tool effectively in a complex environment with many siblings.

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 schema provides no parameter details. The description implies a 'specific schema' but doesn't explain the parameters (subject, context, registry) or their relationships. It adds minimal semantic value beyond the tool name, resulting in a baseline score due to the lack of schema 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 action ('Count') and resource ('number of versions for a specific schema'), making the purpose unambiguous. It doesn't explicitly differentiate from sibling tools like 'count_schemas' or 'get_schema_versions', but the specificity of counting versions for a particular schema is inherently distinct.

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, such as 'get_schema_versions' (which likely lists versions) or 'count_schemas' (which counts schemas, not versions). It also lacks context on prerequisites or constraints, leaving usage unclear.

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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