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luis-dominguez-stori

OpenSearch Logs MCP Server

search_errors

Find error logs in OpenSearch by environment, service, or custom query to troubleshoot issues across development and production systems.

Instructions

Search for error logs, optionally filtered by service or additional query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
environmentYesEnvironment to search: 'dev'/'prod' (iOS) or 'android-dev'/'android-prod' (Android)
serviceNameNoOptional service name to filter by
queryNoAdditional free-text query to apply
timeRangeNoTime range to search within (default: 1h)
sizeNoMaximum number of results to return (default: 50)
Behavior2/5

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

No annotations are provided, so the description carries full burden. It states it searches error logs with optional filtering, but doesn't disclose behavioral traits like whether this is a read-only operation, rate limits, authentication needs, pagination behavior, or what the return format looks like. For a search tool with 5 parameters and no annotations, this is a significant gap.

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?

Single sentence, zero waste. Efficiently states purpose and key optional features without unnecessary elaboration.

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

Completeness2/5

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

For a search tool with 5 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain return values, error handling, or behavioral constraints, leaving significant gaps for an AI agent to understand how to use it effectively.

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 all 5 parameters thoroughly with descriptions and enums. The description adds minimal value beyond implying filtering capabilities, matching the baseline 3 when schema does the heavy lifting.

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 clearly states the verb ('Search') and resource ('error logs'), with optional filtering by service or query. It distinguishes from some siblings like 'get_field_values' or 'get_sample_log' by focusing on errors, but doesn't explicitly differentiate from 'search_logs' or 'search_by_service' which might overlap.

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?

No guidance on when to use this tool versus alternatives like 'search_logs', 'search_by_service', or 'search_by_trace'. The description mentions optional filtering but doesn't provide context for choosing between these search tools or specify prerequisites.

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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