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Log10x MCP Server

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Customer metrics query

log10x_customer_metrics_query
Read-onlyIdempotent

Query customer metrics backend with raw PromQL to explore labels, run one-off expressions, or verify metrics for cross-pillar investigations.

Instructions

Low-level PromQL passthrough to the customer metric backend configured via LOG10X_CUSTOMER_METRICS_URL. Returns the raw Prometheus response shape plus metadata about which backend served the query. This is the escape hatch for cross-pillar investigations the higher-level primitives don't cover — use it to explore the customer backend's label universe, run a one-off PromQL expression, or verify that a specific metric exists before correlating against it. For typical cross-pillar workflows, compose the deterministic primitives: log10x_metrics_that_movedlog10x_rank_by_shape_similaritylog10x_metric_overlay.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNoEnd of the range window. Required when mode=range.
modeNo`instant` runs a point-in-time query; `range` runs over a time window with a bucket step.instant
stepNoBucket step for range queries, in seconds. Required when mode=range.
startNoStart of the range window — ISO8601 or UNIX seconds. Required when mode=range.
promqlYesPromQL expression to execute against the customer metric backend. Example: `apm_request_duration_p99{service="payments-svc"}`.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo
toolYes
viewNosummary
imagesNo
actionsNo
summaryYes
warningsNo
truncatedNo
next_cursorNo
render_hintNo
generated_atYes
schema_epochYes
schema_versionYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.30.7

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint and openWorldHint, so the safety profile is covered. The description adds real context beyond that: it discloses the return shape (raw Prometheus response plus backend metadata) and the env-var-configured backend target. It stops short of noting auth requirements or rate limits.

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?

Front-loaded with the core purpose, then usage framing, then the alternative. Every sentence carries information, though the piece is on the longer side for a passthrough tool.

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?

For a five-parameter passthrough tool with an output schema, the description covers purpose, backend resolution, return shape, correct usage cases and the alternative workflow. Nothing an agent needs in order to choose or call it is missing.

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

Parameters3/5

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

Schema description coverage is 100% and the schema already documents mode, start, end, step and promql with examples. The description adds no parameter-level detail beyond that, so the baseline 3 applies.

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?

States a specific verb and resource: a low-level PromQL passthrough to the customer metric backend. It explicitly positions itself against siblings by calling itself 'the escape hatch' and naming the deterministic primitive chain it is not.

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?

Gives explicit when-to-use cases (explore the label universe, run a one-off PromQL expression, verify a metric exists) and an explicit alternative path (`log10x_metrics_that_moved` → `log10x_rank_by_shape_similarity` → `log10x_metric_overlay`) for typical workflows.

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