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datadog-mcp-server

get-monitors

Read-only

List and filter Datadog monitors by name, tags, or state to quickly find relevant monitors and their current status.

Instructions

List Datadog monitors with optional filtering by name, tags, or state

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoFilter monitors by name substring
tagsNoComma-separated tags. Example: env:prod,team:backend
monitorTagsNoComma-separated service/custom tags
groupStatesNoFilter by group states: all, alert, warn, no data
pageSizeNoNumber of results per page (default 50)
pageNoPage number (0-based)
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.
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the description only needs to add behavioral context. It correctly notes the listing nature and optional filtering, but fails to mention pagination behavior (page, pageSize) or that the result may be large. It does not contradict annotations.

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 a single, efficient sentence that front-loads the action and resource. No unnecessary words or repetition. Every part earns its place.

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 no output schema and a parameter count of 7, the description is somewhat brief. It does not specify what the returned list contains (e.g., monitor IDs, names, statuses) or any sorting/ordering. However, the tool name and filtering hints provide enough context for an AI agent to infer common behavior. More detail on pagination would improve completeness.

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 baseline is 3. The description adds minimal value beyond the schema by summarizing filters as 'name, tags, or state', but does not elaborate on 'extractFields', 'pageSize', or 'page'. It does not enhance understanding of the more complex parameters.

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 action ('List') and resource ('Datadog monitors') with optional filtering. It distinguishes from the sibling 'get-monitor' (which retrieves a single monitor) by implying a list operation. However, it only mentions three filtering dimensions (name, tags, state) while the schema includes seven parameters, including pagination and field extraction, so the purpose is clear but slightly under-specified.

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

Usage Guidelines2/5

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

No explicit guidance is given on when to use this tool versus alternatives. While the sibling list tools (e.g., 'list-hosts') differ by resource, there is no mention of when listing monitors is appropriate, what prerequisites exist, or when to prefer 'get-monitor' instead. The description relies entirely on implicit understanding.

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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