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TANTIOPE

Datadog MCP Server

logs_indexes

Configure Datadog log indexes: set query filters, retention, exclusion filters, and daily limits. Reorder indexes and retrieve configurations.

Instructions

Manage Datadog Logs indexes (filters, retention, exclusion filters, daily limits). Actions: list, get, update, reorder, get_order. Datadog identifies indexes by 'name', not 'id'. Note: create/delete are UI-only per Datadog and not supported through the API. Mutations (update, reorder) are blocked when the server is in read-only mode.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYesAction to perform
nameNoIndex name (required for get/update). Datadog identifies indexes by name, not id.
configNoIndex configuration (for update). Requires filter.query and numRetentionDays. Exclusion filters are forwarded unchanged.
index_namesNoOrdered index name list (required for reorder)
verboseNoReturn full SDK payload alongside summary (default false)
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses that mutations (update, reorder) are blocked in read-only mode and that create/delete are UI-only. It clarifies that Datadog identifies indexes by 'name' not 'id', aiding correct usage. However, it could explicitly mention that updates are mutating and may have 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?

The description is four sentences with clear structure: first sentence states purpose, second lists actions, third clarifies identifier, fourth notes constraints. No wasted words, front-loaded with essential information.

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?

While the description covers actions, parameters, and constraints, it lacks information about return values or output format for each action. With no output schema, the tool would benefit from brief hints (e.g., 'list returns an array of indexes'). The description is adequate but has a clear gap in explaining what each action returns.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

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 adds significant meaning: it notes that Datadog identifies indexes by 'name', not 'id'; for update, it specifies that 'config' requires filter.query and numRetentionDays, and that exclusion filters are forwarded unchanged. This goes well beyond the schema's parameter descriptions.

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 manages Datadog Logs indexes, listing specific actions (list, get, update, reorder, get_order). It distinguishes from sibling tools like logs_archives and logs_pipelines by focusing specifically on index operations.

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 specifies the available actions and notes that create/delete are not supported via API, guiding the agent away from unsupported operations. It also warns that mutations are blocked when the server is in read-only mode, providing clear usage context.

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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