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Zabbix MCP Server

configuration_importcompare

Read-only

Compare Zabbix import data against existing configuration to preview what would be created, updated, or deleted. Validate before applying changes.

Instructions

Compare import data with existing Zabbix configuration without applying changes. Returns a diff showing what would be created, updated, or deleted. Useful for dry-run validation before an actual import.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYesComparison parameters. Same structure as configuration.import: 'format', 'source', and 'rules'.
serverNoTarget Zabbix server. Available: default. Defaults to 'default' if omitted.
raw_jsonNoStrips the security disclaimer preamble from the response so it is pure JSON. Default: false. Requires the bearer token to have 'allow_raw_json' explicitly enabled by an operator in the admin portal - tokens without that policy receive a PolicyError instead. WARNING: setting true bypasses the prompt-injection mitigation marker that wraps untrusted Zabbix data (host names, item descriptions, problem text). LLM clients (Claude, GPT, Cursor, ...) should leave this false; only programmatic non-LLM consumers (Python scripts, n8n workflows that json.loads the result) should set true.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.41.0

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so safety is covered. The description's 'without applying changes' largely restates readOnlyHint, though it usefully previews the diff semantics. It says nothing about auth requirements or error behavior, and the significant security warning for raw_json lives only in the schema.

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?

Three tight sentences, front-loaded with the core action and scope, then the return shape, then the use case. No filler or redundancy.

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

Completeness4/5

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

With an output schema present, the description needn't detail return values, and annotations plus full schema coverage handle most remaining burden. It is essentially complete for correct invocation, though it omits any mention of the raw_json token-policy requirement.

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 100%, including detailed docs for raw_json and the nested params. The description adds no parameter-level detail beyond what the schema provides, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (compare) and both operands (import data vs existing Zabbix configuration), and specifies the output shape (diff of create/update/delete). An agent immediately knows this is a non-mutating dry-run tool, distinct from configuration_export and the various *_get readers.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

'Useful for dry-run validation before an actual import' gives clear when-to-use context. It stops short of naming the actual import counterpart as an alternative or stating exclusions, so it is strong but not fully explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.