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proliant-mcp

proliant-mcp

by hjma29
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Get Event Log

get_event_log
Read-onlyIdempotent

Retrieves recent Integrated Management Log or iLO Event Log entries from an HPE ProLiant server, with optional severity filtering and result limits.

Instructions

Newest entries from the IML or iLO event log, optionally filtered by severity.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "server": "<server>"
    },
    "name": "get_event_log"
  }
}

Input Schema

NameRequiredDescriptionDefault
logNoiml = Integrated Management Log (server hardware events); iel = iLO Event Log.iml
limitNo
serverYesServer name from list_servers (or its iLO host/IP).
severityNoOnly entries of this severity.

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, non-destructive and open-world behavior, so the safety profile is covered. The description adds one genuinely useful behavioral fact beyond that: entries are returned newest-first. It does not disclose what happens when limit is exceeded or whether results are truncated, which matters for log retrieval.

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?

A single tight sentence with the key scope facts front-loaded and no wasted wording. It is efficient, though its brevity leaves the gaps flagged in the other dimensions.

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

Completeness3/5

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

With an output schema present and annotations covering safety, the description needs only to cover purpose and selection guidance. It is adequate for a simple read tool but omits when to prefer it over get_health and says nothing about result limits or truncation.

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 75%, with log, server and severity well documented in the schema. The description's 'newest entries' phrasing usefully implies that limit selects the most recent N rather than the oldest, a detail the schema does not state, but nothing else about parameter behavior is added.

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

Purpose4/5

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

The description names a specific resource ('entries from the IML or iLO event log') and clarifies the two log sources, so an agent knows exactly what is returned. It does not distinguish this from related tools such as get_health, which likely also surfaces hardware conditions, so sibling differentiation is absent.

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

Usage Guidelines2/5

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

There is no statement of when to use this tool versus alternatives like get_health or get_server_summary, and no exclusions. The only contextual cue is 'optionally filtered by severity,' which describes input rather than usage conditions.

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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