Server Quality Checklist

Tool Scores

• create_predictionB
  3.3/5.0
  Behavior3/5

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

  With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals this is a write operation ('Create a prediction') that makes an API call ('sending a question to a specific chatflow'), and mentions potential error responses. However, it lacks details about authentication requirements, rate limits, side effects, or what constitutes a successful prediction. The mention of 'raw JSON response' is helpful but vague.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is appropriately concise with three focused sentences. The first states the purpose, the second explains parameters, and the third describes returns. There's no wasted text, though the structure could be slightly improved by integrating the parameter explanations more naturally rather than using 'Args:' formatting.

  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?

  For a write operation with no annotations and no output schema, the description provides basic but incomplete context. It covers the core action and parameters adequately, but lacks information about authentication, error handling specifics, response format details beyond 'raw JSON,' and how this interacts with the sibling 'list_chatflows' tool. The absence of output schema increases the burden on the description.

  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?

  With 0% schema description coverage, the description must compensate for the schema's lack of parameter documentation. It successfully explains both parameters: 'chatflow_id' as 'The ID of the chatflow to use' with its default, and 'question' as 'The question or prompt to send to the chatflow.' This adds meaningful context beyond the bare schema, though it doesn't elaborate on format constraints or examples.

  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?

  The description clearly states the tool's purpose: 'Create a prediction by sending a question to a specific chatflow or assistant.' It specifies the verb ('create a prediction') and resource ('chatflow or assistant'), though it doesn't explicitly distinguish from the sibling tool 'list_chatflows' (which appears to be a read operation vs. this write operation).

  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?

  The description provides no guidance on when to use this tool versus alternatives. It mentions 'specific chatflow or assistant' but doesn't explain how to choose between them or when to use this versus other prediction tools. There's no mention of prerequisites, constraints, or typical use cases.

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

• list_chatflowsA
  3.9/5.0
  Behavior4/5

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

  With no annotations provided, the description carries the full burden. It discloses important behavioral traits: it respects optional whitelisting/blacklisting configuration via environment variables, and specifies the return format ('JSON-encoded string of filtered chatflows'). This adds valuable context beyond basic functionality, though it doesn't cover aspects like error handling or performance characteristics.

  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 perfectly concise and well-structured: three sentences that each earn their place. The first states the core purpose, the second adds important configuration context, and the third specifies the return format. No wasted words, front-loaded with essential information.

  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?

  Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is quite complete. It covers what the tool does, important configuration behavior, and the return format. For a list operation with no parameters, this provides sufficient context, though it could potentially mention pagination or ordering if relevant.

  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 tool has 0 parameters with 100% schema description coverage. The description doesn't need to compensate for any parameter gaps. It appropriately focuses on behavioral aspects rather than parameter documentation. The baseline for 0 parameters is 4, and the description meets this standard.

  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?

  The description clearly states the tool's purpose: 'List all available chatflows from the Flowise API.' It specifies the verb ('List') and resource ('chatflows'), but doesn't explicitly differentiate from its sibling 'create_prediction' beyond the obvious list vs. create distinction. The description is specific but lacks explicit sibling comparison.

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

  Usage Guidelines3/5

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

  The description implies usage context through the mention of whitelisting/blacklisting configuration, suggesting this tool is for retrieving chatflows with optional filtering. However, it doesn't provide explicit guidance on when to use this tool versus alternatives or any prerequisites. The usage is implied rather than explicitly stated.

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