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zabbix-mcp-server

itemprototype_get

Retrieve item prototypes from Zabbix by specifying item IDs, discovery rule IDs, or host IDs. Filter and format results in JSON for efficient monitoring integration.

Instructions

Get item prototypes from Zabbix with optional filtering.

Args:
    itemids: List of item prototype IDs to retrieve
    discoveryids: List of discovery rule IDs to filter by
    hostids: List of host IDs to filter by
    output: Output format
    search: Search criteria
    filter: Filter criteria
    
Returns:
    str: JSON formatted list of item prototypes

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
discoveryidsNo
filterNo
hostidsNo
itemidsNo
outputNoextend
searchNo

Implementation Reference

  • The handler function for the 'itemprototype_get' MCP tool. It is decorated with @mcp.tool() which also serves as registration. The function calls the Zabbix API to retrieve item prototypes based on the provided parameters and returns a formatted JSON response.
    @mcp.tool()
    def itemprototype_get(itemids: Optional[List[str]] = None,
                          discoveryids: Optional[List[str]] = None,
                          hostids: Optional[List[str]] = None,
                          output: Union[str, List[str]] = "extend",
                          search: Optional[Dict[str, str]] = None,
                          filter: Optional[Dict[str, Any]] = None) -> str:
        """Get item prototypes from Zabbix with optional filtering.
        
        Args:
            itemids: List of item prototype IDs to retrieve
            discoveryids: List of discovery rule IDs to filter by
            hostids: List of host IDs to filter by
            output: Output format (extend or list of specific fields)
            search: Search criteria
            filter: Filter criteria
            
        Returns:
            str: JSON formatted list of item prototypes
        """
        client = get_zabbix_client()
        params = {"output": output}
        
        if itemids:
            params["itemids"] = itemids
        if discoveryids:
            params["discoveryids"] = discoveryids
        if hostids:
            params["hostids"] = hostids
        if search:
            params["search"] = search
        if filter:
            params["filter"] = filter
        
        result = client.itemprototype.get(**params)
        return format_response(result)
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 tool retrieves data ('Get'), implying it's a read operation, but doesn't mention authentication requirements, rate limits, error handling, or pagination. The description lacks details on what 'JSON formatted list' entails (e.g., structure, fields). For a tool with no annotations, this leaves significant behavioral gaps.

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 well-structured and concise: a purpose statement followed by parameter explanations and return value. Each sentence adds value without redundancy. It could be slightly more front-loaded by emphasizing key use cases, but overall it's efficient and clear.

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, no output schema), the description is minimally adequate. It covers the purpose and parameters but lacks behavioral context, usage guidelines, and detailed output explanation. Without annotations or output schema, users must rely on external knowledge for full understanding, leaving gaps in completeness.

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?

The description lists all 6 parameters with brief labels (e.g., 'itemids: List of item prototype IDs to retrieve'), adding basic semantics beyond the schema's 0% coverage. However, it doesn't explain parameter interactions, default values (e.g., 'output' defaults to 'extend'), or provide examples. With low schema coverage, this partial compensation earns a baseline score.

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: 'Get item prototypes from Zabbix with optional filtering.' It specifies the verb ('Get'), resource ('item prototypes'), and domain ('Zabbix'). However, it doesn't explicitly differentiate from sibling tools like 'item_get' or 'discoveryrule_get', which would require more specific context about what distinguishes item prototypes from regular items or discovery rules.

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 mentions 'optional filtering' but doesn't specify scenarios, prerequisites, or comparisons to sibling tools like 'item_get' or 'discoveryrule_get'. Without this context, users must infer usage from the tool name alone.

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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