Find Prometheus time series by label matchers or selector, optionally filtered by start and end timestamps, to inspect available metrics.
Find series by label matchers
| Name | Required | Description | Default |
|---|---|---|---|
| end | No | End timestamp (RFC3339 or Unix timestamp) | |
| match | Yes | Series selector (e.g. 'up', 'http_requests_total{job="prometheus"}') | |
| start | No | Start timestamp (RFC3339 or Unix timestamp) |
Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure but only states what the tool does, not how it behaves. It doesn't mention whether this is a read-only operation, what permissions are needed, how results are returned (format, pagination), error conditions, or performance characteristics. This is inadequate for a tool with parameters and no annotation coverage.
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 phrase with zero wasted words. It's appropriately sized for a simple tool and front-loads the core purpose immediately. Every word earns its place in this minimal but complete statement.
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 tool has 3 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what 'series' means in this Prometheus context, what the return format looks like, or any behavioral aspects. For a data query tool with multiple parameters, this leaves significant gaps in understanding how to effectively use it.
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%, so the schema fully documents all three parameters (match, start, end) with good descriptions. The description adds no additional parameter semantics beyond implying label matching through 'label matchers', which is already covered in the schema's match parameter description. This meets the baseline for high 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 'Find series by label matchers' clearly states the action (find) and target (series), but is vague about what 'series' refers to in this context (Prometheus time series data) and doesn't distinguish this tool from siblings like 'get-label-values' or 'instant-query'. It provides basic purpose but lacks specificity and differentiation.
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 like 'instant-query' or 'range-query' from the sibling list. It doesn't mention prerequisites, exclusions, or typical use cases, leaving the agent with no contextual usage information beyond the basic purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.