Log10x MCP Server

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Explain enforcement mode

log10x_explain_mode

Read-onlyIdempotent

Explains the selected log enforcement mode in service-level plain language with dollar math, so you understand the impact before applying.

Instructions

CALL THIS AFTER log10x_cost_options when the user picks a mode. Explains the chosen enforcement mode (drop / sample / compact / tier_down / offload / observe_only) in service-level plain language, with dollar math when byte metrics are available. Returns must_render_verbatim (plain-text three-section card, no markdown), must_ask_user (Apply or Preview choice), and forbidden_next_actions (locks apply tools and log10x_preview_filter until the user picks). routes_to.apply routes all non-observe modes to log10x_configure_engine; observe_only has routes_to.apply === null (no enforcement step). routes_to.preview routes to log10x_preview_filter with { service, mode }.

Input Schema

TableJSON Schema

Name	Required	Description
`mode`	Yes	Which enforcement mode to explain. Keep-everything levers come first, then the lossy opt-ins. `compact` = keeps everything: engine minifies events ~50-80% losslessly; all events still reach the stack. `offload` = keeps everything: engine diverts matched events to a customer-owned S3 bucket; readable via log10x_retriever_query. `tier_down` = keeps everything: engine stamps the routeState marker; a routing rule moves those events to a cheaper storage tier (Datadog Flex / CloudWatch IA / Azure Monitor Basic or Auxiliary Logs). `sample` = lossy opt-in: engine passes 1-in-N events through to the stack; the rest are discarded. `drop` = lossy opt-in: engine hard-drops matched patterns at the Receiver before delivery. `observe_only` = engine observes and fingerprints but does not act; use to baseline volume before committing.
`service`	Yes	Service name to personalize the explanation with service-level volume and cost figures.
`destination`	No	Auto-detected destination stack or forwarder. When omitted the tool infers from envs.json / env vars. Used to name the specific vendor in the explanation ("your Datadog workspace", "your Splunk index", etc.).
`effective_ingest_per_gb`	No	Customer-supplied $/GB rate used for the dollar overlay. When set, source_disclosure.rate_source='customer_supplied'. When absent, the shared rate resolver falls back to envs.json analyzerCost → LOG10X_ANALYZER_COST → destination list price → unset (no dollar overlay).

Output Schema

TableJSON 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.

First observedv1.30.7Sep 11, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Goes well beyond the readOnly/idempotent annotations by disclosing runtime contract details: must_render_verbatim (plain-text card, no markdown), must_ask_user (Apply/Preview), and forbidden_next_actions that lock apply tools and log10x_preview_filter until the user chooses. This tells the agent exactly how to render and gate the response, which the annotations cannot convey.

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 trigger condition and core purpose, and every sentence carries routing or rendering information. It is very dense in a single long second sentence, which slightly taxes readability but wastes no words.

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 coordination tool in a multi-step flow, the description covers preconditions, expected output shape, user-gating, and post-call routing to both apply and preview paths. Despite an output schema existing, the extra contract detail makes the tool fully actionable.

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 coverage is 100% and the schema already richly documents every parameter, including the full mode enum definitions. The description adds only the routing-shaped { service, mode } usage for preview, so with structured data doing the heavy lifting this sits at the baseline.

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+resource ('Explains the chosen enforcement mode') and enumerates the exact modes it covers. It also names the sibling it must follow (log10x_cost_options) and the siblings it routes to (log10x_configure_engine, log10x_preview_filter), so an agent can distinguish it from those tools without opening any schema.

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?

Explicit sequencing: 'CALL THIS AFTER log10x_cost_options when the user picks a mode.' It also specifies downstream routing per mode (apply for non-observe, null for observe_only, preview always), giving clear when-to-use and what-follows guidance.

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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