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mpeirone

zabbix-mcp-server

by mpeirone

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.1

  • Disambiguation5/5

    The three tools have clearly distinct roles: execute API calls, get documentation, and list methods. No overlap in functionality.

    Naming Consistency5/5

    All tools follow the consistent 'zabbix_api_<suffix>' pattern (the main tool is 'zabbix_api' as the base).

    Tool Count4/5

    With 3 tools, the set is minimal but well-scoped for a generic Zabbix API wrapper. Each tool is necessary and sufficient.

    Completeness5/5

    The set covers discovery, documentation, and execution of any Zabbix API method. No gaps for the stated purpose.

  • Average 4.5/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • 1 of 2 community issues answered or closed in the last 6 months
    • 0 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available
  • This repository is licensed under GPL 3.0.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
      "$schema": "https://glama.ai/mcp/schemas/server.json",
      "maintainers": [
        "your-github-username"
      ]
    }

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior4/5

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

    Discloses that methods are 'discovered dynamically from Zabbix API', adding behavioral context beyond the schema. No annotations provided, description carries burden well but lacks mention of potential network call or idempotency.

    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?

    Three sentences are front-loaded with purpose and usage, but third sentence is slightly redundant. Still clear and well-structured.

    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?

    Given simplicity (one optional parameter, output schema exists), description fully covers purpose, usage context, and discovery nature. No gaps for an agent to invoke 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% and description only paraphrases the schema's parameter description ('e.g., 'host', 'item'. If omitted, returns all'). Adds no novel semantic insight beyond what the schema already provides.

    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?

    Description clearly states 'Get available Zabbix API objects and methods' with a specific verb and resource. Distinguishes from siblings zabbix_api and zabbix_api_docs by indicating it lists available methods before use.

    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?

    Explicitly says 'Call this to discover what API methods are available before using zabbix_api()', providing direct guidance on when to use and suggesting it as a prerequisite for the sibling tool.

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

  • Behavior4/5

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

    No annotations provided, so description carries full burden. It discloses that the tool shows parameter information but does not explicitly state it is read-only or non-destructive. However, the phrase 'Get documentation' strongly implies a safe query, and the context of sibling tools reinforces this.

    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 sentences with no wasted words. First sentence states purpose, second provides usage guidance, third details output. Front-loaded and efficient.

    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?

    Complete for a documentation retrieval tool. With an output schema present and sibling tools clearly differentiated, the description covers when and what. No gaps identified.

    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% with descriptions for all 3 parameters. The description adds no new parameter-specific info beyond the schema, but that is acceptable given the schema richness. Baseline 3 is appropriate.

    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?

    The description clearly states the tool gets Zabbix API method documentation, using specific verb 'Get' and resource 'Zabbix API method documentation'. It distinguishes from sibling tools by positioning itself as a preparatory call before zabbix_api().

    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?

    Explicitly instructs to call this tool BEFORE zabbix_api() when unsure about parameters. Provides clear context for use and no exclusions needed.

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

  • Behavior4/5

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

    With no annotations, the description sufficiently discloses that the tool requires multiple iterations, returns JSON, and can perform both read and write operations (as shown in examples). It does not cover rate limits or authentication, but given it is a generic wrapper, the information is adequate.

    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 lengthy but well-structured with sections for workflow, common patterns, and examples. It front-loads the core purpose and is organized logically. While it could be slightly more concise, the detail is justified by the tool's complexity.

    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?

    Given the tool's generic nature and the existence of sibling tools, the description is highly complete: it explains the iterative workflow, provides parameter details, includes real-world examples, references companion tools, and describes the return format. It leaves no critical gaps.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema coverage is 0%, yet the description adds rich semantics: explains method format ('object.action') with examples, describes params as optional with a note on 'output' for get operations, and provides multiple concrete examples illustrating usage.

    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?

    The description clearly states that the tool executes Zabbix API methods, with the verb 'Execute' and resource 'Zabbix API method'. It distinguishes itself from sibling tools (zabbix_api_docs and zabbix_api_list) by being the execution tool, and the workflow explicitly mentions using those for guidance.

    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?

    The description provides explicit workflow steps: call zabbix_api_docs or zabbix_api_list when unsure, then use this tool, and iterate if needed. It also advises broadening searches on empty results and gives common patterns, making it clear when to use this tool versus alternatives.

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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