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datadog-mcp-server

slo-compliance-snapshot

Aggregates SLO health data including error budget and compliance status from config, history, corrections, and monitors in a single request.

Instructions

Aggregated SLO health: config + history-window SLI + active corrections + each linked monitor's current state in one call. Computes errorBudgetRemainingPct and status (compliant | at-risk | breached). Replaces 3-5 round-trips of get-slo + get-slo-history + list-slo-corrections + get-monitor (per linked monitor). Uses Promise.allSettled — partial failures populate caveats[] instead of crashing. Renders an Apps SDK card on ChatGPT clients (Claude clients receive the same JSON text).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sloIdYesSLO ID. Example: abc123def456abc123def456abc123de
historyDaysNoDays of history to evaluate SLI against target (default 7, max 90)
extractFieldsNoComma-separated dotted paths to project from response (e.g. 'id,name,owner.name,columns.*.name'). Use `*` as wildcard for arrays/objects. Wrap field names with dots in backticks. Reduces response tokens dramatically on large entities.
Behavior4/5

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

With no annotations, the description discloses key behavioral traits: uses Promise.allSettled for partial failures, populates caveats[], and renders a card on ChatGPT clients. Missing explicit mention of read-only or permission needs, but overall strong.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured, with each sentence adding value: purpose, alternatives, behavioral details, and rendering info. No fluff.

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?

Given no output schema, the description explains return values (errorBudgetRemainingPct, status, caveats) and behavior on partial failure. It also covers platform-specific rendering. Complete for a tool with 3 parameters.

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%, so baseline is 3. The description does not add significant new meaning beyond the schema descriptions for parameters like sloId, historyDays, and extractFields.

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?

The description clearly states it aggregates multiple SLO-related data into one call, computes errorBudgetRemainingPct and status, and distinguishes itself from sibling tools like get-slo, get-slo-history, list-slo-corrections, and get-monitor by combining their functionality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly says it replaces 3-5 round-trips and lists the alternative tools, giving clear context for when to use this tool. However, it doesn't explicitly state when not to use it or mention any prerequisites.

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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