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librenms-mcp

by mhajder
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Python
Hybrid

Arp Search

arp_search
Read-onlyIdempotent

Search LibreNMS ARP entries by IP address, MAC address, CIDR notation, or retrieve all entries for a device. Supports pagination with limit and offset.

Instructions

Retrieve ARP entries from LibreNMS by search query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of results to return
queryYesSearch string for ARP entries. Supports IP address, MAC address, CIDR notation, or "all" (use with device parameter for all entries on a device)
offsetNoNumber of results to skip (offset) for pagination

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.10.3
    • addedInput schema / properties / limit
      Added value: +{
+  "default": 100,
+  "description": "Maximum number of results to return",
+  "minimum": 1,
+  "type": "integer"
+}
    • addedInput schema / properties / offset
      Added value: +{
+  "default": 0,
+  "description": "Number of results to skip (offset) for pagination",
+  "minimum": 0,
+  "type": "integer"
+}
  2. First observedv1.9.0

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds little behavioral context beyond the resource and query mechanism, but no contradiction exists and the output schema covers return structure.

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?

A single front-loaded sentence states the action, resource, and mechanism with zero wasted words. It is appropriately sized for a tool whose schema and annotations carry most operational detail.

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?

For a simple read-only search tool with full schema coverage and an output schema, the description is nearly complete. It could add a brief note about supported query types or pagination behavior, but those are already present in the parameter descriptions.

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 the schema already documents query, limit, and offset. The description's phrase 'by search query' aligns with the query parameter but does not add meaning beyond what the schema 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?

The description states a specific verb and resource: 'Retrieve ARP entries from LibreNMS by search query.' The resource 'ARP entries' is distinct from sibling tools like fdb_lookup or ports_search_mac, so an agent can differentiate it without opening the schema.

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

Usage Guidelines3/5

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

The description implies usage when ARP entries are needed, and the schema details supported query formats like IP, MAC, and CIDR. However, it does not explicitly compare against alternative lookup tools, provide exclusions, or state when a different tool should be preferred.

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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