Fetch a single log by its logId, in full: all attributes flattened in (plus a
resource object) and the message untruncated — no length cap. Use after a lean
logs scan when you need the complete body of one row.
No verbose/maxStringChars knobs here — this tool always returns everything, uncapped. For custom column selection use run_sql.
Returns: the log object.
| Name | Required | Description | Default |
|---|---|---|---|
| logId | Yes | ULID of the log to fetch |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility. It transparently discloses key behaviors: returns everything uncapped, no length cap, flattened attributes plus resource object, and no verbose/maxStringChars knobs. It could mention error handling (e.g., invalid logId) but the core behavioral traits are well covered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded with the primary action. Each sentence adds value: purpose, usage context, behavior clarification, and a return type. No redundant or filler content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with no output schema or annotations, the description is complete. It covers what the tool returns, when to use it, its full-fetch behavior, and an alternative for column selection. Everything needed for correct invocation is present.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (logId described as 'ULID of the log to fetch'). The description adds no further parameter detail beyond the schema, but it reinforces the purpose of the parameter in context. This meets the baseline for full schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches a single log by logId, with a specific verb and resource. It distinguishes itself from sibling tools like `logs` (lean scan) and `run_sql` (custom column selection), making its purpose unique.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit usage guidance is provided: 'Use after a lean `logs` scan when you need the complete body of one row.' It also gives an alternative tool for different needs ('For custom column selection use run_sql'). This fully addresses when and when not to use the tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tool pairs are near-duplicates, including three deprecated aliases (add_investigation_alert_channel vs add_alert_channel, list_investigation_alert_channels vs list_alert_channels, remove_investigation_alert_channel vs remove_alert_channel) that muddy the surface. Additionally, suppress_signal and create_ignore_rule both suppress alerting via different mechanisms, which could cause misselection despite detailed descriptions.
The vast majority of tools follow a clear verb_noun snake_case pattern (create_api_test, list_issues, set_alert_rule_status). A few bare-noun tools (logs, spans, metrics) and the standalone verb correlate break the pattern slightly, but overall the naming is highly consistent and predictable.
With 52 tools, this is on the extreme end of the calibration scale. Even accounting for the broad scope of an observability platform, the count is excessive and includes several deprecated redundancies that inflate it further.
The toolset provides comprehensive CRUD/lifecycle coverage across all major domains: alert rules (create, read, update, delete, status, delivery, preview), API tests (create, read, update, delete, run history, credentials), ignore rules and suppressions, issues with digest config, investigations with claim/read, channels, credentials, and rich query tools (logs, spans, metrics, SQL, traces, correlation). No obvious dead ends or missing core operations.