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Python

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List Observability Patterns

list_observability_patterns

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List clustered log patterns with counts and compare them against the previous window to find frequent or changing errors; filter by service, severity, or pattern.

Instructions

List log patterns (clustered log templates) with counts against the previous window of the same length. Only service:, severity: and pattern: filters apply; others are echoed in meta.ignored_filters. Each pattern's logs_query drills into matching rows with GET /v1/observability/logs; it is omitted when that query would exceed the /logs length or clause limits. Requires a personal or Global API key (Team API Keys are group-scoped and get 403 unsupported_api_key) or an OAuth token with the obs.logs:read (or all) scope, and the Observability logs read permission. A Global API key uses its service user's Observability role, which starts as the team's default Observability role. Limited to 60 requests per minute per token; on this endpoint the X-RateLimit-* headers report that per-minute budget, not the default API limit. Every 429 returns the errors envelope with code rate_limited and a Retry-After header.

Input Schema

Name	Required	Description	Default
`sort`	No	`count` for the most frequent patterns, `change` for the biggest change against the previous window.	count
`filter_to`	No	Window end: ISO8601 or relative like `now`. Defaults to `now`.
`page_size`	No	Number of top patterns to return (1-25, default 15), ordered by sort. Not paginated: meta.truncated is true when more patterns matched; narrow filter[query] to see them.
`filter_from`	No	Window start: ISO8601 or relative like `now-6h`. Defaults to `now-1h`. At most 7 days long, within the last 30 days.
`filter_query`	No	Query using `service:`, `severity:` and `pattern:` filters, e.g. `service:checkout severity:error`.
`filter_flagged`	No	Only patterns flagged by a detector (new, spike, error surge).

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Addedv2.4.0Nov 2, 2026

TDQS

A4.5/5.0

Behavior5/5

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

It adds extensive behavioral detail beyond the annotations: required API key or OAuth scopes, Team API Key 403 behavior, rate limit of 60 requests per minute, per-endpoint X-RateLimit header semantics, and 429 response format with Retry-After. It also discloses filter behavior and logs_query omission conditions, fully covering operational traits.

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?

The description is dense but well-structured and front-loaded, starting with the core purpose before detailing filters, auth, and rate limits. Every sentence contributes necessary operational information for calling the tool correctly.

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 the tool's complexity, output schema, and annotation coverage, the description is complete. It covers authentication, permissions, rate limits, filter behavior, and error handling, so an agent has what it needs to invoke the tool correctly without additional explanation of return values.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds meaningful parameter semantics by stating that only service:, severity: and pattern: filters apply and that unsupported filters are echoed in meta.ignored_filters. This is useful context not present in the schema.

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 opens with a specific verb and resource: it lists log patterns, defined as clustered log templates, with counts against the previous window. This clearly distinguishes it from raw log listing or counting tools by emphasizing patterns and window-over-window comparison.

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

Usage Guidelines3/5

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

The description provides clear context for pattern analysis and explains filter applicability, but it does not explicitly state when to use this tool versus siblings like list_observability_logs or count_observability_logs. Usage is implied rather than directly routed.

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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