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Zabbix MCP Server

infrastructure_summary_get

Read-only

Get a whole-Zabbix snapshot of host, item, trigger, and template counts plus active-problem severity. See top problematic hosts in one call.

Instructions

Whole-Zabbix dashboard summary: host / item / trigger / template counts + active-problem severity breakdown + top-N most-problematic hosts, in a single call.

Use this for 'show me the overall status' / 'is everything OK?' / 'how many problems do we have right now?' style first-look prompts. Replaces five separate count + filter calls and the LLM correlation step that local models often get wrong.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
top_nNoNumber of top-problematic hosts to include (default 5).
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

A4.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds the aggregation/replaces-many-calls framing, but says nothing about auth requirements, rate limits, or the security-disclaimer preamble and prompt-injection marker that only appear inside the raw_json schema text. Adequate but not rich beyond annotations.

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?

Two short paragraphs, front-loaded with the concrete payload contents before the usage guidance. Every sentence earns its place with no filler or repetition.

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, return-value detail is unnecessary, and the description covers purpose, contents, and usage well. The one notable gap is that the raw_json security caveat (bypassing the prompt-injection mitigation marker, PolicyError without operator policy) is significant for LLM consumers yet surfaced only in the schema, not the description.

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%, so all three parameters (top_n, server, raw_json) are fully documented in the schema itself. The description only echoes 'top-N most-problematic hosts' and adds no syntax, format, or constraint detail beyond what the schema already provides — 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+resource ('Whole-Zabbix dashboard summary') and enumerates exactly what it returns: host/item/trigger/template counts, active-problem severity breakdown, and top-N most-problematic hosts. This clearly distinguishes it from the many single-resource *_get siblings that each return one entity type.

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

Usage Guidelines5/5

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

Gives explicit trigger conditions with quoted example prompts ('show me the overall status', 'is everything OK?') and names the alternative pattern it replaces: 'five separate count + filter calls and the LLM correlation step'. The condition that selects this tool over the sibling count/filter tools is unambiguous.

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