Server Quality Checklist

Tool Scores

• fal_cancel_requestA
  4.5/5.0
  Behavior4/5

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

  Annotations indicate mutability (readOnlyHint=false) and destructiveness (destructiveHint=true). The description adds behavioral details: error if wrong status, 404 for invalid request_id, and confirmation on success. However, idempotentHint=true is not fully addressed as the behavior on re-cancellation of an already cancelled request is not specified.

  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?

  Approximately 150 words with clear sections (description, Args, Returns, Examples, Error Handling). Front-loaded with the core action. Every sentence adds value without redundancy.

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

  Completeness5/5

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

  For a simple cancellation tool with two required params and no output schema, the description fully covers purpose, usage conditions, error cases, and expected return. No gaps remain.

  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?

  Both parameters have schema descriptions covering 100% of parameters. The description's Args section adds only slight reinforcement of the schema, not substantial new meaning. Baseline of 3 is appropriate.

  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?

  Clearly states the action (cancel), target (queued request), and condition (before finishing). Distinguishes from siblings like fal_check_status and fal_submit_request by specifying the cancellation scope and status requirement.

  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 mentions the only valid status (IN_QUEUE), provides examples of when to use and not use, and suggests using fal_check_status first to confirm status. This gives clear guidance and excludes inappropriate usage.

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

• fal_list_modelsA
  4.7/5.0
  Behavior4/5

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

  Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety. The description adds behavioral details like error messages ('No models found', 'Authentication failed') and pagination behavior, which go beyond annotations.

  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 well-organized with clear sections (Args, Returns, Examples, Error Handling). Each sentence is purposeful and adds information, 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.

  Completeness5/5

  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, but the description fully explains the return format for both JSON and markdown, pagination with next_cursor, and error handling. It covers all necessary context for correct usage.

  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?

  Schema coverage is 100%, so baseline is 3. The description adds value by providing example values for query and category, clarifying defaults for limit and response_format, and explaining the cursor for pagination. This extra context justifies a 4.

  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 clearly states the tool's purpose: search or browse fal.ai's catalog of 600+ hosted models to find the right model ID. It explicitly distinguishes itself from siblings by saying 'Does NOT run any model — this is discovery only.' The verb+resource is specific and unique.

  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?

  Provides explicit when-to-use examples (e.g., query='video' for video generation) and when-not-to-use (if model_id is known, use fal_run_model or fal_submit_request). This directly guides the agent on alternatives, earning top marks.

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

• fal_run_modelA
  4.8/5.0
  Behavior5/5

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

  Discloses blocking behavior, absence of queue, timeout of 5 minutes, and error handling outcomes. No contradiction with annotations (readOnlyHint=false, destructiveHint=false).

  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?

  Well-structured with clear sections (main description, Args, Returns, Examples, Error Handling). Slightly lengthy but each section provides necessary detail.

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

  Completeness5/5

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

  Covers all three parameters, return value (model-specific), examples, error conditions, and directs users to related tools. Adequate given no output schema.

  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?

  Schema coverage is 100%, but description adds examples for model_id and arguments, explains response_format options, and provides context beyond schema definitions.

  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 clearly states it runs a fal.ai model synchronously and blocks for the result. It specifies the verb 'Run' and the resource 'fal.ai model', and distinguishes from the asynchronous alternative fal_submit_request.

  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 says when to use (fast models) and when not to (slow models), provides examples, and directs users to fal_submit_request for long-running jobs and fal_get_model_schema for unknown model fields.

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

• fal_submit_requestA
  4.8/5.0
  Behavior5/5

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

  The description discloses key behaviors: it submits to a queue and returns immediately without waiting for the result. It also covers error handling (authentication, invalid input, not found) and which parameters are optional. Annotations already indicate readOnlyHint: false and destructiveHint: false, and the description aligns with and enriches them with concrete error scenarios.

  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 fairly long but well-structured with clear sections (Args, Returns, Examples, Error Handling). It front-loads the essential purpose and usage distinction. While every sentence is informative, the length could be slightly trimmed without losing value. Still, the organization aids comprehension.

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

  Completeness5/5

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

  Given the tool's complexity (4 parameters, async workflow, sibling tools), the description is complete: it covers input, output format, error handling, and usage workflow. Even without an output schema, it documents return fields for both response formats. Examples and error cases ensure an agent can use it correctly.

  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?

  Schema coverage is 100%, so baseline is 3. However, the description adds value beyond the schema by providing examples (e.g., model_id 'fal-ai/minimax/video-01' with arguments 'prompt'), explaining the purpose of webhook_url, and specifying default for response_format. It also ties error handling to parameters (e.g., invalid input requires checking fal_get_model_schema). This additional context justifies a 4.

  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 begins with a clear verb+resource: 'Submit a job to fal.ai's async queue and return immediately with a request_id.' It explicitly distinguishes itself from fal_run_model (blocking) and mentions use cases for slow models like video generation or training jobs. This makes its purpose unambiguous and differentiated from sibling tools.

  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?

  The description provides explicit when-to-use (slow models, non-blocking) and when-not-to-use (fast models, use fal_run_model) guidance. It also instructs on the subsequent steps: use fal_check_status and fal_get_result after submission. This is comprehensive and actionable for an AI agent.

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

• fal_check_statusA
  4.9/5.0
  Behavior5/5

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

  Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds context: it does not return output, must call fal_get_result when COMPLETED, and details error responses (404, auth failure). No contradictions.

  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?

  Well-structured with clear sections (Args, Returns, Examples, Error Handling). Every sentence adds value. Could be slightly more concise, but overall front-loaded and easy to scan.

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

  Completeness5/5

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

  For a simple status-check tool, the description is fully complete: covers parameters, return object structure with fields and types, usage flow, error cases. No missing information needed for correct invocation.

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

  Parameters5/5

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

  Schema coverage is 100%, so baseline 3. The description adds significant value with examples for model_id, clarifies request_id purpose, includes default values and formatting options, and explains include_logs and response_format beyond 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 clearly states it checks the status of a previously submitted fal.ai request and explicitly says it does NOT return final output, distinguishing it from fal_submit_request and fal_get_result. The verb 'check' and resource 'status' are specific.

  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?

  Provides explicit when-to-use ('when you have a request_id'), when-not-to-use ('haven't submitted yet', 'want actual output'), and alternatives (fal_submit_request, fal_get_result). The examples section reinforces this.

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

• fal_encode_file_as_data_uriA
  4.9/5.0
  Behavior5/5

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

  Annotations already indicate read-only, idempotent, non-destructive. Description adds that it's a local offline operation with no network contact, and details error messages for missing or oversized files.

  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?

  Very concise yet comprehensive: sections for description, args, returns, examples, error handling. Front-loaded with main purpose, no fluff.

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

  Completeness5/5

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

  For a simple tool with one parameter and no output schema, the description fully explains use cases, return value format, error handling, and limitations. No gaps.

  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?

  Schema covers file_path with description (100% coverage). Description adds context like absolute path, examples of file types, and references to error handling, surpassing the schema baseline.

  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?

  Clearly states it reads a local file and returns a base64 data URI, with explicit purpose to pass into fal model arguments. Distinguishes from sibling tools which are about model inference or status.

  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?

  Provides explicit when-to-use (local file as model input) and when-not-to-use (already hosted URL, files >10MB). Gives alternatives (pass URL directly) and error conditions.

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

• fal_get_model_schemaA
  5/5.0
  Behavior5/5

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

  Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint as safe. Description adds error handling details: returns 'Not found (404)' for invalid model_id and 'Authentication failed' for missing/invalid FAL_KEY. Discloses output format based on response_format. No contradictions.

  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?

  Well-structured with clear sections: purpose, Args, Returns, Examples, Error Handling. Each sentence adds value, no fluff. Front-loaded with main purpose and usage context. Appropriate length for the complexity.

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

  Completeness5/5

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

  Given the tool's simplicity (2 parameters, no output schema, rich annotations), the description covers all necessary aspects: purpose, parameters, usage guidelines, error handling, and examples. No gaps. The agent can fully understand when and how to use the tool.

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

  Parameters5/5

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

  Schema description coverage is 100%, but description adds significant value: provides multiple examples for model_id, explains the difference between markdown and json response_format, and clarifies the meaning of each parameter in context. Also explains how response_format affects return values.

  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 it fetches the OpenAPI schema for a specific fal.ai model, showing input fields and output. It clearly distinguishes from siblings like fal_run_model, fal_submit_request, and fal_list_models by saying 'Call this before fal_run_model or fal_submit_request whenever you're unsure of a model's required arguments.'

  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 states when to use (before running a model if unsure of arguments) and when not to use (if you already know the model's arguments). Provides concrete examples like getting a 422 error. Also mentions alternatives implicitly via sibling context.

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

• fal_get_resultA
  5/5.0
  Behavior5/5

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

  Annotations indicate readOnlyHint, idempotentHint true, and destructiveHint false. The description confirms the tool fetches (read-only) and idempotently returns the same result, and adds context about error conditions (404, auth failure) that annotations don't cover. No contradictions.

  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 well-structured with sections for Args, Returns, Examples, Error Handling. Each sentence is informative and not redundant. It is concise yet comprehensive, front-loading key usage guidance.

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

  Completeness5/5

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

  Given no output schema, the description explains return structure (model-specific payload) and format differences (markdown vs json). It also covers prerequisites (status must be COMPLETED), error handling, and includes examples. This provides complete context for using the tool.

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

  Parameters5/5

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

  Schema coverage is 100%, but description adds meaning: provides an example for model_id (e.g., 'fal-ai/minimax/video-01'), explains request_id's origin from fal_submit_request, and explains the response_format options with human vs. machine usage. This adds value beyond 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 clearly states it fetches the final output of a queued request. It distinguishes from sibling tools by specifying it is used after submission and status check, and references fal_submit_request and fal_check_status, making its purpose unambiguous.

  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 instructs to call only after fal_check_status reports COMPLETED, warns against calling earlier, and provides error handling info. This gives clear when-to-use and when-not-to-use guidance.

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