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TANTIOPE

Datadog MCP Server

slos

Create, list, update, delete, and track Datadog Service Level Objectives (SLOs) with error budget and SLI status. Monitor reliability compliance and performance targets.

Instructions

Manage Datadog Service Level Objectives. Actions: list (with SLI status & error budget), get, create, update, delete, history. SLO types: metric-based, monitor-based. Each list/get/create/update response includes a url field deep-linking to the Datadog UI. Use for: reliability tracking, error budgets, SLA compliance, performance targets.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYesAction to perform
idNoSLO ID (required for get/update/delete/history)
idsNoMultiple SLO IDs (for list with specific IDs)
queryNoSearch query (for list)
tagsNoFilter by tags (for list)
limitNoMaximum number of SLOs to return (default: 50)
configNoSLO configuration (for create/update). Must include type, name, thresholds.
fromNoStart time for history (ISO 8601 or relative like "7d", "1w")
toNoEnd time for history (ISO 8601 or relative, default: now)
Behavior2/5

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

No annotations are provided, so the description must fully disclose behavioral traits. It mentions that responses include a 'url' field and lists SLO types, but lacks details on destructive actions (e.g., delete irreversibility), permissions, or rate limits. This is insufficient for a tool with create/update/delete actions.

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?

The description is concise (two sentences) and front-loaded with key actions and types. Every sentence contributes meaningful information, making it efficient for the agent to parse.

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

Completeness3/5

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

Given the absence of an output schema and the complexity of 9 parameters, the description covers basic functionality (actions, types, URL field) but lacks details on error handling, idempotency, or partial update behavior. It is adequate but not fully comprehensive for a multi-action tool.

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%, so the schema already documents all parameters. The description adds marginal value by noting that list includes SLI status and error budget, but does not significantly enhance understanding beyond the schema. Baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool manages SLOs and lists specific actions (list, get, create, etc.) and SLO types. It distinguishes the tool within the broader Datadog domain, but does not explicitly differentiate from sibling tools beyond the subject matter.

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

Usage Guidelines3/5

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

The description provides use cases like 'reliability tracking, error budgets, SLA compliance, performance targets,' giving context. However, it does not specify when to avoid using this tool or mention alternative tools for similar tasks, leaving the agent without clear guidance on selection.

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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