Store (create or overwrite) the authenticated customer's Datadog monitoring credentials. ADMIN only. apiKey and appKey are required; apiUrl is optional. This tool never returns the stored value back — only a confirmation message.
| Name | Required | Description | Default |
|---|---|---|---|
| apiKey | Yes | Datadog API key. | |
| apiUrl | No | Datadog API base URL. Omit to keep the default. | |
| appKey | Yes | Datadog application key. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It discloses the create/overwrite side effect, the ADMIN-only permission requirement, and the non-return of stored values (only a confirmation message). These are meaningful behavioral traits beyond the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with the primary purpose, and every sentence carries essential information: action, permission, required/optional params, and return behavior. No redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple credential-setting tool with no annotations and no output schema, the description covers all critical aspects: the action, permission, parameter requirements, and return behavior. It is self-sufficient and gives an agent enough context to invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 repeats that apiKey and appKey are required and apiUrl is optional, but this adds no new meaning over the schema. No additional parameter semantics are provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Store (create or overwrite)') and the resource ('Datadog monitoring credentials'), making it specific and distinguishable from sibling tools like set_investigation_cloudwatch_credentials. The ADMIN only note also clarifies authorization scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for setting Datadog credentials but does not explicitly name alternatives (e.g., set_investigation_cloudwatch_credentials or create_api_test_credential). It does provide a clear usage constraint (ADMIN only) and lists required/optional parameters, but the 'when to use vs alternatives' aspect is only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tool pairs are near-duplicates, including three deprecated aliases (add_investigation_alert_channel vs add_alert_channel, list_investigation_alert_channels vs list_alert_channels, remove_investigation_alert_channel vs remove_alert_channel) that muddy the surface. Additionally, suppress_signal and create_ignore_rule both suppress alerting via different mechanisms, which could cause misselection despite detailed descriptions.
The vast majority of tools follow a clear verb_noun snake_case pattern (create_api_test, list_issues, set_alert_rule_status). A few bare-noun tools (logs, spans, metrics) and the standalone verb correlate break the pattern slightly, but overall the naming is highly consistent and predictable.
With 52 tools, this is on the extreme end of the calibration scale. Even accounting for the broad scope of an observability platform, the count is excessive and includes several deprecated redundancies that inflate it further.
The toolset provides comprehensive CRUD/lifecycle coverage across all major domains: alert rules (create, read, update, delete, status, delivery, preview), API tests (create, read, update, delete, run history, credentials), ignore rules and suppressions, issues with digest config, investigations with claim/read, channels, credentials, and rich query tools (logs, spans, metrics, SQL, traces, correlation). No obvious dead ends or missing core operations.