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ClaudioLazaro

MCP Datadog Server

create_slo_corrections

Generate SLO corrections to adjust service level objectives in Datadog, enabling precise monitoring and management of system performance metrics.

Instructions

Create an SLO Correction.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

No annotations are provided, so the description carries full burden. 'Create' implies a write/mutation operation, but the description doesn't disclose any behavioral traits: no information about required permissions, whether this is idempotent, what happens on failure, rate limits, or what the response contains. For a mutation tool with zero annotation coverage, this is a significant gap in behavioral disclosure.

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 a single four-word sentence, which is technically concise. However, it's under-specified rather than efficiently informative - it doesn't earn its place by adding value beyond the tool name. While not verbose, it fails to provide necessary context that would justify its brevity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool ('create') with no annotations, no output schema, and no parameters, the description is incomplete. It doesn't explain what an SLO Correction is, what data might be needed (despite empty schema), what the operation returns, or any prerequisites. The agent lacks sufficient context to understand when and how to use this tool effectively.

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% description coverage (empty schema). With no parameters to document, the description doesn't need to compensate for schema gaps. The baseline for 0 parameters is 4, as there's no parameter semantics to explain beyond what the empty schema already indicates.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Create an SLO Correction' restates the tool name with minimal elaboration. It identifies the verb ('Create') and resource ('SLO Correction'), but lacks specificity about what an SLO Correction entails or how it differs from related tools like 'create_slos' or 'update_slo_correction'. This is a tautological restatement rather than a clear purpose definition.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

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. With sibling tools like 'create_slos', 'update_slo_correction', and 'delete_slo_correction', there is no indication of the specific context for creating corrections versus creating SLOs themselves or modifying existing corrections. The agent receives zero usage context.

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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