Skip to main content
Glama
Sign In
Log Pruner MCP Server

Log Pruner MCP Server

by nrapendra-singh
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_errors

Retrieve detailed error logs from OpenSearch or local JSON files, including stack traces, log bodies, and pod information for troubleshooting non-200 responses.

Instructions

Get full detail for non-200 responses (errors). Shows stack traces, log bodies, pod info — everything needed for troubleshooting.

Args: path: Path to local OpenSearch JSON file. index: OpenSearch index to query (requires OPENSEARCH_URL env). query: Additional Lucene query for API mode. time_from: ISO timestamp lower bound. time_to: ISO timestamp upper bound. limit: Max error entries to return (default 20). context_before: Number of log entries to show before each error (default 0). Set to 20 to see what happened leading up to the error.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_errors"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathNo
indexNo
limitNo
queryNo
time_toNo
time_fromNo
context_beforeNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description must fully disclose behavior. It reveals output content (stack traces, log bodies) and mentions two modes (local file vs OpenSearch index) with an environment variable requirement. However, it omits error handling (e.g., if both path and index are provided), return structure, and potential side effects.

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?

Description is front-loaded with a clear purpose statement followed by structured Args list. It is not overly verbose, though the Arg descriptions could be slightly more concise. Overall efficiently conveys necessary information.

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?

Given the tool's complexity (7 parameters, two data sources, output schema present), the description covers core behavior but lacks details on parameter interactions (e.g., mutual exclusivity of path and index) and edge cases. The presence of an output schema reduces the need to explain return values, but behavioral gaps remain.

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 description coverage is 0%, so the description carries full parameter explanation. It thoroughly describes all 7 parameters, including types, defaults, and contextual details (e.g., 'ISO timestamp', 'requires OPENSEARCH_URL env'). This compensates fully for the missing schema descriptions.

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 retrieves full details for non-200 responses (errors), including stack traces, log bodies, and pod info. This distinguishes it from sibling tools like query_logs or read_logs which are general-purpose log retrieval.

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?

The description does not provide guidance on when to use this tool versus alternatives like query_logs or read_logs. It lacks explicit when-not-to-use instructions or comparisons to siblings, forcing the agent to infer usage from the name and context.

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

Deploy Server

Other Tools