Ops MCP Server

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

No annotations are provided, so the description carries the full burden. It effectively discloses behavioral traits: it's a read-only operation (implied by 'Get detailed information'), it returns comprehensive details (state, config, resource usage, S3 log paths), and it provides hints for related tools. However, it doesn't mention potential errors, rate limits, or authentication requirements, which keeps it from a perfect score.

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 well-structured and front-loaded: the first sentence states the core purpose, followed by specifics on what information is returned, parameter details, and important usage notes. Every sentence adds value—none are redundant or vague—making it efficient and easy to parse.

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 tool's complexity (3 parameters, 0% schema coverage, no annotations) and the presence of an output schema, the description is complete. It explains the purpose, parameters, return value overview, and usage guidelines. The output schema will handle detailed return values, so the description doesn't need to enumerate them further, making it appropriately comprehensive.

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 0%, so the description must compensate. It fully documents all three parameters: application_id (with source guidance: 'from Airflow initialise task log'), job_run_id (with source: 'from Airflow processing task log'), and env (with enum values: 'dev', 'uat', 'test', or 'prod' and critical usage instructions). This adds significant meaning beyond the bare schema.

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 tool's purpose: 'Get detailed information about a specific EMR Serverless job run.' It specifies the exact resource (EMR Serverless job run) and verb (get detailed information), distinguishing it from sibling tools like list_job_runs (which lists multiple runs) or get_emr_cost_summary (which focuses on costs).

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 provides explicit guidance on when to use this tool: for detailed information about a specific job run, including config, resource usage, and logs. It distinguishes from alternatives by mentioning 'ready-to-use hints for read_spark_driver_log and browse_s3_logs,' implying those are separate tools for deeper log inspection. It also includes a critical exclusion: 'IMPORTANT: Do NOT guess or default. Ask the user which environment if not specified.'

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