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ClaudioLazaro

MCP Datadog Server

send_logs

Transmit logs to Datadog for monitoring and analysis via HTTP API, supporting compression and batch processing with size limits.

Instructions

Send your logs to your Datadog platform over HTTP. Limits per HTTP request are:

  • Maximum content size per payload (uncompressed): 5MB

  • Maximum size for a single log: 1MB

  • Maximum array size if sending multiple logs in an array: 1000 entries

Any log exceeding 1MB is accepted and truncated by Datadog:

  • For a single log request, the API truncates the log at 1MB and returns a 2xx.

  • For a multi-logs request, the API processes all logs, truncates only logs larger than 1MB, and returns a 2xx.

Datadog recommends sending your logs compressed. Add the Content-Encoding: gzip header to the request when sending compressed logs. Log events can be submitted with a timestamp that is up to 18 hours in the past.

The status codes answered by the HTTP API are:

  • 202: Accepted: the request has been accepted for processing

  • 400: Bad request (likely an issue in the payload formatting)

  • 401: Unauthorized (likely a missing API Key)

  • 403: Permission issue (likely using an invalid API Key)

  • 408: Request Timeout, request should be retried after some time

  • 413: Payload too large (batch is above 5MB uncompressed)

  • 429: Too Many Requests, request should be retried after some time

  • 500: Internal Server Error, the server encountered an unexpected condition that prevented it from fulfilling the request, request should be retried after some time

  • 503: Service Unavailable, the server is not ready to handle the request probably because it is overloaded, request should be retried after some time

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It excels by detailing critical behavioral traits: payload size limits (5MB max, 1MB per log), truncation behavior for oversized logs, compression recommendations, timestamp constraints (up to 18 hours in the past), and comprehensive HTTP status codes with retry guidance. This provides the agent with essential operational context beyond basic functionality.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose but becomes verbose with detailed limits and status codes. While this information is valuable, it could be more structured (e.g., using bullet points or sections). Some sentences, like the list of status codes, are lengthy and could be condensed without losing clarity, reducing overall conciseness.

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?

Given the tool's complexity (sending logs with specific constraints) and the absence of annotations and output schema, the description is largely complete. It covers behavioral details, limits, and error handling. However, it lacks information on authentication requirements (e.g., API key usage) and does not mention sibling tools, leaving minor gaps in contextual understanding.

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

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately focuses on behavioral aspects rather than parameters. It does not add parameter semantics, but this is acceptable given the empty schema, warranting a baseline score of 4 for not introducing confusion.

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's purpose: 'Send your logs to your Datadog platform over HTTP.' It specifies the verb ('send'), resource ('logs'), and destination ('Datadog platform'), making the action explicit. However, it does not distinguish this from sibling tools like 'logs_send' or 'logs_search_events', which could cause confusion in tool selection.

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?

The description provides no guidance on when to use this tool versus alternatives. It lists technical limits and status codes but does not mention sibling tools like 'logs_send' or 'logs_search_events', nor does it specify use cases or prerequisites. This leaves the agent without context for choosing this tool over others.

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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