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datadog-mcp-server

slo-compliance-snapshot

Read-only

Aggregate SLO health, compute error budget and compliance status (compliant, at-risk, breached) in one API call, replacing multiple round-trips.

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.
Behavior5/5

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

Beyond annotations (readOnlyHint, openWorldHint), the description adds critical behavior: use of Promise.allSettled to handle partial failures via caveats[], rendering an Apps SDK card on ChatGPT clients, and computing specific fields. This significantly aids agent understanding of side effects.

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?

Four sentences, front-loaded with key purpose, followed by efficiency gains, technical behavior, and output format. No fluff or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers purpose, behavior, and output format (card/JSON). Without an output schema, it could detail response structure more, but it sufficiently informs an agent about what to expect.

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% with parameter descriptions. The description adds no extra semantic value beyond the schema, such as elaborating on extractFields usage or historyDays default. Baseline of 3 is appropriate.

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 the tool aggregates SLO health data, including config, history, corrections, and linked monitor states, computing errorBudgetRemainingPct and status. It distinguishes from siblings like get-slo, get-slo-history, and list-slo-corrections by noting it replaces 3-5 round-trips.

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?

The description explicitly compares to individual tools, stating it replaces multiple round-trips and uses Promise.allSettled for partial failures. It implies when to use (for a comprehensive snapshot) but does not explicitly state when not to use or provide alternative tools.

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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