Retrieve all Service Level Objective corrections to monitor and adjust performance metrics in Datadog.
Get all Service Level Objective corrections.
This tool declares no required parameters. Add optional parameters from the input schema as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {},
"name": "get_slo_corrections_v1"
}
}| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states 'Get all' but doesn't disclose behavioral traits like whether this is a read-only operation, if it requires authentication, potential rate limits, pagination behavior, or what format the data returns. The description is too minimal to guide an agent on how the tool behaves beyond the basic action.
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?
The description is a single, efficient sentence with no wasted words. It's front-loaded with the core action ('Get all Service Level Objective corrections') and avoids unnecessary elaboration. Every word earns its place in conveying the basic purpose.
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?
Given the complexity of SLO corrections in a monitoring context, no annotations, no output schema, and 0 parameters, the description is incomplete. It doesn't explain what SLO corrections are, how the data is structured, if there are limitations (e.g., time ranges, organization scope), or what the return format looks like. For a tool that likely returns a list of corrections, more context is needed for effective use.
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?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter information, which is appropriate given the lack of parameters. A baseline of 4 is applied since the schema fully covers the absence of parameters, and the description doesn't need to compensate.
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 'Get all Service Level Objective corrections' states a clear verb ('Get') and resource ('Service Level Objective corrections'), but it's vague about scope and lacks differentiation from sibling tools like 'get_slo_correction' (singular) and 'get_slo_corrections_v1_2'. It doesn't specify what 'all' means in terms of filtering or pagination.
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?
No guidance is provided on when to use this tool versus alternatives. With sibling tools including 'get_slo_correction' (singular) and 'get_slo_corrections_v1_2', the description fails to indicate whether this is the primary list tool, a bulk retrieval method, or how it differs from similar endpoints. No prerequisites or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.