Skip to main content
Glama
bgrgndzz

datadog-logs-mcp

by bgrgndzz

search_logs

Search Datadog logs with a query, time range, and filters to retrieve matching entries and their attributes, timestamps, and metadata.

Instructions

Search Datadog logs using the log search syntax. Returns matching log entries with their attributes, timestamps, and metadata.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNoEnd time - ISO 8601 datetime or relative like "now"now
fromNoStart time - ISO 8601 datetime or relative like "now-1h", "now-15m"now-15m
sortNoSort order: timestamp (oldest first) or -timestamp (newest first)-timestamp
limitNoMax number of logs to return (1-1000, default 50)
queryYesLog search query using Datadog log search syntax (e.g. "service:web-app status:error")
cursorNoPagination cursor from a previous search result to get next page
indexesNoSpecific log indexes to search. Defaults to all indexes.
Behavior3/5

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

With no annotations, the description carries the transparency burden. It discloses the return shape (matching log entries with attributes, timestamps, metadata) which is helpful. However, it does not mention pagination behavior, time range default semantics, or potential rate limits. It provides adequate but not rich behavioral insight.

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 one concise sentence that front-loads the action and result. No filler or redundancy; every word earns its place.

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 moderate complexity (7 params, no output schema, no annotations), the description adequately covers the core behavior and return type. It does not detail pagination responses, but the schema defines cursor and limit inputs, which is acceptable. The lack of output schema is mitigated by the description's mention of returned fields.

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 does not add meaning beyond the schema for parameters like query, from, to, or limit. It reinforces the use of log search syntax but that is already stated in the schema's query parameter description.

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 uses a specific verb ('Search') and resource ('Datadog logs'), and clarifies the search syntax usage. It clearly distinguishes from siblings like get_log (single log retrieval) and aggregate_logs (aggregations) by focusing on raw log search results.

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 implies usage: use this tool to search for logs matching a query. However, it does not explicitly mention when to use this versus sibling tools like aggregate_logs or get_log, nor provide exclusions or alternative recommendations. There is clear context but no contrast.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/bgrgndzz/datadog-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server