get_loki_series

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves series but doesn't describe the return format (e.g., JSON structure), potential errors (e.g., invalid selectors), rate limits, or authentication needs. For a read operation with no annotation coverage, this is a significant gap in transparency.

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 a single, efficient sentence that front-loads the core purpose ('Get series') and includes key details (label combinations, selectors, datasource). There is no wasted verbiage or redundancy, making it highly concise and well-structured for quick understanding.

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?

Given the complexity (a read operation with 4 parameters, no annotations, and no output schema), the description is incomplete. It lacks information on the return values (e.g., what the series data looks like), error handling, and usage context compared to siblings. For a tool with no output schema, this omission is particularly problematic.

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%, with all parameters well-documented in the schema (e.g., 'datasourceUid' as the Loki datasource UID, 'match' as series selectors). The description adds no additional parameter semantics beyond what the schema provides, such as examples of label matchers or time format details. Baseline 3 is appropriate when the schema does the heavy lifting.

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 action ('Get series') and the resource ('from a Loki datasource'), specifying it retrieves label combinations matching selectors. It distinguishes itself from siblings like 'get_loki_labels' or 'query_loki' by focusing on series retrieval rather than labels or querying logs. However, it doesn't explicitly differentiate from 'get_prometheus_series', which might be a similar tool for a different datasource.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a valid datasource UID), exclusions, or comparisons to sibling tools like 'get_loki_labels' (for labels) or 'query_loki' (for log queries). This leaves the agent to infer usage based on the name and parameters alone.

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