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Zabbix MCP Server

problem_active_get

Read-only

Fetches active Zabbix problems, filtering out disabled triggers/hosts and low-severity events, so you see only actionable issues.

Instructions

Real, actionable problems right now -- the PRIMARY tool for an LLM asked "what is wrong on the Zabbix server?".

Returns only problems where BOTH the trigger AND the host are enabled, with severity Warning (2) or above. Skips the noise that problem_get would return: stale alerts on disabled triggers, problems on hosts that were taken out of monitoring but not deleted, and Information / Not classified events that operators do not act on.

Each problem comes back with the host name, severity_label ("warning", "high", "disaster", ...), and time rendered as a human-readable UTC string. Use this whenever the operator asks "active problems", "current issues", "what is firing", "real problems", or "skip disabled".

For an unfiltered raw view (e.g. to also see stale problems), use problem_get instead.

Returns {"problems": [...], "count": N, "filtered_out": M} where filtered_out = problems dropped due to disabled trigger/host.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax problems to return after filtering disabled triggers/hosts (default 50).
serverNoTarget Zabbix server. Available: default. Defaults to 'default' if omitted.
hostidsNoRestrict to problems on these hosts.
groupidsNoRestrict to problems on hosts in these host groups.
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.
sortfieldNoProblem sort key; problem.get can only sort by 'eventid' (default).eventid
sortorderNo'ASC' or 'DESC' (default 'DESC').DESC
severitiesNoSeverity floor as a list of numeric codes. Default [2,3,4,5] (Warning and above). Pass [0,1,2,3,4,5] to include Information / Not classified.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.41.0

TDQS

A4.6/5.0
Behavior5/5

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

Annotations only declare readOnlyHint and openWorldHint; the description goes well beyond by disclosing the exact filter semantics (BOTH trigger and host enabled, severity Warning+), what is deliberately excluded, and what filtered_out counts. This is the kind of operational context annotations cannot carry.

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?

Front-loaded with the core purpose and filtering behavior, then the return shape, then the sibling routing. Slightly long relative to what is strictly required, but each sentence adds distinct information with no filler.

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

Completeness5/5

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

An output schema exists, yet the description still summarizes the return shape (problems/count/filtered_out) and the enriched fields (host name, severity_label, human-readable UTC time). Combined with the filter rules and sibling routing, an agent has everything needed to call and interpret it correctly.

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 coverage is 100%, so the schema already documents all eight parameters including the severities default and the raw_json policy warning. The description reinforces only the severity floor ('Warning (2) or above') and says nothing new about limit, hostids, groupids, sortfield, or raw_json. Baseline 3 is appropriate when the schema does the heavy lifting.

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 (return real, actionable problems) and explicitly positions itself as the primary tool for 'what is wrong on the Zabbix server?'. It directly contrasts with problem_get, so an agent can distinguish it from its closest sibling without opening any schema.

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?

Names the exact user phrasings that select it ('active problems', 'current issues', 'what is firing', 'real problems') and the condition that selects the alternative ('unfiltered raw view, e.g. to also see stale problems -> problem_get'). When-to-use and when-to-use-something-else are both explicit.

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