Server Quality Checklist

Tool Scores

• ground_kafka_topicA
  4.1/5.0
  Behavior4/5

  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 discloses the sampling behavior, the requirement of a reachable broker and populated topic, and what it returns. This goes beyond a simple read assertion, though it does not mention failure modes or potential side effects (which are minimal for a read/sampling tool).

  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 sentences, front-loading the main function and then providing usage and requirements. Every sentence earns its place with actionable information and no fluff.

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

  Completeness4/5

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

  With 5 parameters, no output schema, and no annotations, the description covers the tool's purpose, return type, prerequisites, and downstream usage. It lacks details on error handling or parameter-dependent behavior, but for the tool's complexity and available structured fields, it is sufficiently complete. The requirement that 'the topic populated' hints at failure conditions.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters2/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema description coverage is only 40%, and the description does not compensate. Parameters like topic, sample_size, and watermark_delay_seconds lack explanation beyond their raw schema names. The description mentions 'event_time' indirectly but doesn't explain how it is used. No additional meaning is provided over the schema.

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

  Purpose5/5

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

  The description starts with a specific verb ('Discover') and identifies the exact resource ('Kafka topic's schema') and method ('by sampling its messages'). It clearly distinguishes itself from siblings (generate_flink_job, deploy_to_kafka) by stating its result is 'a schema + sample rows ready to pass to generate_flink_job'.

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

  Usage Guidelines4/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description explicitly states when to use it: 'Use when the user references a real topic and you don't have its schema.' It does not explicitly mention alternatives or when not to use, but the usage scenario is clear and contextual. The sibling tools are different enough that no exclusion is needed.

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

• deploy_to_kafkaA
  4.3/5.0
  Behavior4/5

  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 discloses that the SELECT logic is verified on real Flink and that the returned deployable_sql uses an upsert-kafka sink for updating jobs, which is valuable behavioral context beyond the basic purpose.

  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 three sentences with key information front-loaded. Every sentence contributes purpose, behavior, or usage context without redundancy.

  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 tool has 5 parameters and no output schema, requiring more detail. The description explains the high-level flow but omits the semantics of 'request' and 'bootstrap', and does not describe the structure of the returned deployable_sql beyond noting it is a full pipeline.

  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 schema covers sources, sample_data, and sink_topic (60% coverage), but the description does not explain the 'request' or 'bootstrap' parameters, which also lack schema descriptions. The description adds no parameter-specific semantics beyond what the schema already provides.

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

  Purpose5/5

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

  The description states a specific action: 'Produce a deployable topic->topic Flink job' with a clear verb and resource. It further distinguishes from siblings by noting 'Use when the user wants to WRITE results to a Kafka topic, not just query them.'

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

  Usage Guidelines5/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  Explicitly provides the trigger condition: 'Use when the user wants to WRITE results to a Kafka topic.' It contrasts with query-only use, indicating the alternative path, though it doesn't name sibling tools directly.

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

• generate_flink_jobA
  4.4/5.0
  Behavior4/5

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

  With no annotations, the description carries the full burden of behavioral disclosure. It reveals the critical behavior that the tool actually RUNS the job on real Flink (not just generates), and returns the actual output OR the Flink error. This goes beyond a simple schema and gives users an accurate expectation of execution and failure modes.

  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 sentences, front-loaded with the primary action, and every phrase adds critical information (generation, execution, return value, use cases, contrast with hand-written SQL, sample data instruction). No fluff or redundancy.

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

  Completeness4/5

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

  Despite having no output schema, the description explicitly states what the tool returns (verified SQL + actual output or Flink error), covering the return contract. The complex nested schema is fully documented. It lacks only explicit prerequisites or permission requirements, but the description is complete for a code-generation-and-execution tool.

  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 already provides 100% coverage with descriptive parameter details. The description adds further value by emphasizing the need for 'representative sample rows' and clarifying the return contract (verified SQL + output/error). The combination of schema and description gives users complete parameter understanding.

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

  Purpose5/5

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

  The description explicitly states the tool's action: generate and RUN an Apache Flink SQL streaming job, returning verified SQL and output/errors. It clearly distinguishes from siblings (ground_kafka_topic, deploy_to_kafka) by focusing on job generation/execution rather than Kafka infrastructure.

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

  Usage Guidelines4/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  It provides explicit when-to-use guidance ('Use whenever the user wants a Flink streaming job') and even contrasts against hand-written SQL ('produces jobs that provably run, unlike hand-written SQL'). It also gives a practical instruction ('Supply representative sample rows'). It doesn't explicitly state exclusions or alternatives among siblings, but the context is clear enough.

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