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log10x_resolve_batch

Read-onlyIdempotent

Templatize a batch of log events and return per-pattern triage locally, so you can rank patterns by frequency and severity without a deployed pipeline or account.

Instructions

Templatize a batch of log events and return per-pattern triage. This is one of two tools that run a local Log10x pipeline on the user's machine (the other is log10x_extract_templates): call this whenever the user asks 'can you run log10x locally', 'do a local pipeline run', 'templatize these events', or supplies any batch to analyze (Datadog/Splunk/Elastic query results, Slack incident attachments, kubectl logs, raw text). Does NOT need Kubernetes, a deployed Reporter, or a Log10x account: the pipeline runs in-process on the host. Output structures the batch by a paste-local templateHash with per-pattern frequency + severity + full template structure + per-slot variable distribution. Each top pattern carries actions[] chaining to log10x_investigate (historical correlation) and log10x_retriever_query (offload retrieval). Do NOT call for single-line resolution: use log10x_event_lookup for that. REQUIRES a local engine: the tenx binary on PATH, or Docker with LOG10X_TENX_MODE=docker.

Input Schema

Name	Required	Description
`path`	No	Local file path when source=file. Required for file mode.
`text`	No	Raw text blob when source=text: newline-separated log events.
`events`	No	Inline log lines: an array of lines, or a single newline-separated string (both accepted). `source` need not be set.
`source`	No	Optional: auto-inferred from whichever of `events` / `text` / `path` you actually provide, so you normally do not set it. Provide explicitly only to disambiguate when more than one is present.
`environment`	No	Environment nickname: used to build next_actions that call log10x_investigate.
`top_n_patterns`	No	How many patterns to return in the ranked triage.
`include_next_actions`	No	Whether to generate next_action suggestions for each top pattern.

Output Schema

Name	Required	Default
`data`	No
`tool`	Yes
`view`	No	summary
`images`	No
`actions`	No
`summary`	Yes
`warnings`	No
`truncated`	No
`next_cursor`	No
`render_hint`	No
`generated_at`	Yes
`schema_epoch`	Yes
`schema_version`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedv1.30.8Oct 15, 2026

changedInput schema / properties / environment / description

Previous value: -"Environment nickname — used to build next_actions that call log10x_investigate."New value: +"Environment nickname: used to build next_actions that call log10x_investigate."

changedInput schema / properties / source / description

Previous value: -"Optional — auto-inferred from whichever of `events` / `text` / `path` you actually provide, so you normally do not set it. Provide explicitly only to disambiguate when more than one is present."New value: +"Optional: auto-inferred from whichever of `events` / `text` / `path` you actually provide, so you normally do not set it. Provide explicitly only to disambiguate when more than one is present."

changedInput schema / properties / text / description

Previous value: -"Raw text blob when source=text — newline-separated log events."New value: +"Raw text blob when source=text: newline-separated log events."

First observedv1.30.7Sep 11, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Annotations only declare readOnly/openWorld/idempotent; the description adds substantive context the annotations cannot: the pipeline runs in-process on the host, requires no Kubernetes/Reporter/account, and depends on the tenx binary or Docker via LOG10X_TENX_MODE. It also describes the output shape and the actions[] chaining to other tools, well beyond the structured fields.

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-loads the core purpose before layering triggers, exclusions, requirements, and output structure, and every segment carries information. It is dense with parenthetical asides, which slightly taxes readability but avoids filler.

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 7-param, zero-required tool with an output schema and safety annotations, the description covers purpose, invocation triggers, exclusions, runtime prerequisites, and cross-tool chaining. Nothing an agent needs to select or correctly invoke it appears missing.

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 per-parameter semantics are already documented at the baseline 3. The description adds genuine meaning by enumerating accepted input kinds (Datadog/Splunk/Elastic results, Slack attachments, kubectl logs, raw text), which contextualizes the events/text/path parameters and the auto-inferred source behavior.

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?

Opens with a specific verb+resource ('Templatize a batch of log events and return per-pattern triage') and immediately distinguishes itself from siblings by naming the other local-pipeline tool (log10x_extract_templates) and the single-line alternative (log10x_event_lookup). The resource, scope, and mode of operation are unambiguous.

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 trigger phrases ('can you run log10x locally', 'templatize these events'), enumerates the accepted input sources, and states an explicit exclusion with the correct alternative ('Do NOT call for single-line resolution: use log10x_event_lookup'). When-to-use, when-not, and alternatives are all present.

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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