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ThoTischner

observability-mcp

query_logs

Fetch recent log entries for a service with error/warning counts and recurring error patterns. Inspect service behavior or investigate anomalies detected by other tools.

Instructions

Fetch recent log entries for ONE service over a look-back window, with a pre-computed summary (error/warning counts and the most frequent error patterns). When to use: to inspect what a service actually logged, or to investigate an error spike surfaced by detect_anomalies / get_service_health. For numeric metrics use query_metrics instead. Prerequisites: get the exact service name from list_services (the service must expose a logs signal). Behavior: read-only, no side effects. Returns the matching log entries (newest first, capped by limit) plus a summary with total/error/warn counts and top recurring error patterns. No matches yields an empty result with a zeroed summary; an unreachable backend yields a structured explanatory error, never an exception.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
serviceYesRequired. Exact, case-sensitive service name exactly as returned by `list_services` (e.g. 'payment-service').
queryNoOptional. Filter expression matched against the log message; regular expressions are supported. Omit to return all entries in the window.
durationNoOptional. Look-back window ending at 'now', written as <number><unit> with unit s|m|h|d (e.g. '5m', '1h', '24h'). Default: '5m'.
levelNoOptional. Return only entries at this severity. Default: all levels.
limitNoOptional. Maximum number of log entries to return (most recent first). Default: 100.
bypass_redactionNoOptional. When true, request that PII/secret redaction be skipped for this single call. The server only honours this when the calling credential was explicitly authorised via OMCP_KEY_BYPASS_REDACTION; otherwise the request still gets redacted output. Default: false.
Behavior4/5

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

Despite no annotations, the description declares 'Behavior: read-only, no side effects' and explains outcomes for no matches (empty result with zeroed summary) and unreachable backend (structured error). It also notes the bypass_redaction authorization nuance. Could add more on rate limits, but sufficient for transparency.

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 well-structured with clear sections (function, when-to-use, prerequisites, behavior, returns). Each sentence adds value, though slightly verbose. Could be tightened but earns its length.

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 6 parameters and no output schema, the description fully covers the return format (log entries newest first, summary with counts/patterns, empty result, error behavior). It also explains error handling. No 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 100%, and the description adds meaning beyond the schema: reinforces case-sensitivity for 'service', regex support for 'query', format/default for 'duration', and authorization details for 'bypass_redaction'. Each parameter gets contextual clarification.

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 action ('Fetch recent log entries'), the resource ('for ONE service'), and the result ('with a pre-computed summary'). It distinguishes from sibling tools like 'detect_anomalies', 'get_service_health', and 'query_metrics'.

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 states when to use ('to inspect what a service actually logged, or to investigate an error spike'), when not to use ('For numeric metrics use query_metrics instead'), and prerequisites ('get the exact service name from list_services').

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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