MCP Datadog Server

aggregate_logs

Aggregate log events over a time range using count, average, percentile, min, max, or sum to perform statistical analysis of log data.

Instructions

Aggregate log data for a time range using the specified aggregation type (count, avg, percentile, min, max, sum). Useful for statistical analysis of logs.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "filter": "<filter>",
      "from": "<from>",
      "to": "<to>",
      "aggregationType": "<aggregationType>"
    },
    "name": "aggregate_logs"
  }
}

Input Schema

Name	Required	Description
`to`	Yes	End time as Unix timestamp (seconds/ms) or ISO 8601 string
`from`	Yes	Start time as Unix timestamp (seconds/ms) or ISO 8601 string
`filter`	Yes	Log filter query (e.g., "status:error", "service:checkout")
`aggregationType`	Yes	Aggregation function to apply (e.g., "count" for log count, "avg" for average of numeric field)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 2, 2026

TDQS

B3.3/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden. It does not disclose whether the operation is read-only, what the return format is, or any limitations or side effects. It only restates the aggregation operation, leaving key behavioral aspects ambiguous.

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 ('Aggregate log data') and includes essential scoping (time range, aggregation type). No unnecessary words or repetition.

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?

The tool is relatively simple, and the schema covers all parameters. However, with no annotations or output schema, the description lacks information about return values (e.g., single value vs. time series) and any operational constraints. It is adequate for basic selection and invocation but has clear gaps for a fully informed decision.

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 semantic value beyond the schema, such as listing aggregation types already in the enum. It does not clarify how filter interacts with aggregationType or the exact format of time parameters, but the schema already covers these.

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 aggregates log data for a time range using a specified aggregation type, providing a specific verb and resource. It implicitly distinguishes from search_logs by focusing on aggregation rather than raw retrieval, but it does not explicitly name alternatives.

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 phrase 'Useful for statistical analysis of logs' implies when to use the tool, but it does not explicitly contrast with alternatives like search_logs or query_metrics, nor does it provide exclusions or prerequisites. The usage guidance is implied rather than explicit.

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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