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start_performance_case

Read-onlyIdempotent

Create a durable Azure SQL performance benchmark case. SQL is fingerprinted, keeping queries redacted and excluding them from the state database.

Instructions

Create a durable, redacted performance case. SQL is fingerprinted and is not written to the MCP state database.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sqlYesBaseline read-only SELECT-shaped SQL.
objectiveNoPrimary tuning objective: elapsed_time, cpu, logical_reads, or physical_reads.elapsed_time
database_nameNo
idempotency_keyNoOptional caller-generated idempotency key.
parameter_casesNoUp to four named parameter cases, for example common, rare, NULL, and boundary. Each has name, values, types, and positive weight. Values are fingerprinted, not persisted.
query_store_query_idNoOptional exact Query Store query_id. When supplied, evidence collection uses only this identity and never fuzzy matching.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
headlineYes
Behavior1/5

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

The description says 'Create a durable... performance case,' which implies a write/mutation operation, yet the annotations declare readOnlyHint=true. This is a direct annotation contradiction. The added detail about SQL fingerprinting and not writing to the MCP state database is useful, but the contradiction forces a score of 1.

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 two concise, front-loaded sentences with no filler. The first sentence states the purpose and the second adds critical redaction/storage behavior. Every word earns its place.

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

Completeness3/5

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

The description covers durability and redaction, and the output schema plus rich input schema reduce the need to document return values. However, it lacks context around the case lifecycle, idempotency, parameter_case semantics, and how this relates to starting versus tuning sessions, leaving some gaps for a 6-parameter tool.

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?

The input schema covers 83% of parameters with descriptions, so the schema carries most of the semantic load. The description adds the behavioral fact that SQL is fingerprinted, which relates to the sql parameter, but this is marginal and already partially echoed in parameter_cases. Baseline of 3 is appropriate.

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?

Description states a specific verb and resource: 'Create a durable, redacted performance case.' This clearly conveys the tool's core function. However, it does not explicitly distinguish itself from sibling tools like start_tuning_session or get_performance_case, so it stops short of full differentiation.

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?

No guidance is provided on when to use this tool versus siblings such as start_tuning_session, collect_performance_evidence, or get_performance_case. There are no exclusions, prerequisites, or alternative tool references, leaving the agent to infer usage from the name and schema.

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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