Server Quality Checklist

Tool Scores

• generate_plot_securelyA
  4.1/5.0
  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 of behavioral disclosure. It explains the isolated Azure sandbox, the local interception of the base64 payload, and the secure writing of the PNG to the user's home directory. This is considerable transparency, though it does not detail file naming or overwrite behavior.

  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 efficient and well-structured, leading with the core purpose and adding an architecture note. It is slightly longer than necessary but each sentence adds meaningful value, and the guidance is front-loaded.

  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 moderate complexity, the description covers purpose, usage, security architecture, output location, and size constraints. An output schema exists, so return values are not needed. Minor gaps like file naming do not detract significantly from overall completeness.

  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?

  Schema description coverage is 100%, so the schema already documents each parameter. The description adds the array size limit (under 10,000 points) and clarifies the x/y data relationship, but does not significantly enhance parameter semantics 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 the tool 'Generate line or scatter charts from x and y data', specifying both the action and resource. It effectively distinguishes this tool from siblings like execute_code_securely and sanitize_csv_securely by focusing on plot generation.

  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 says 'Use this tool to visually represent numerical trends', providing contextual guidance for when to use it. It also includes a practical constraint about keeping data arrays under 10,000 points to avoid timeouts, but does not explicitly mention alternatives or when not to use it.

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

• execute_code_securelyA
  4.2/5.0
  Behavior4/5

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

  With no annotations, the description carries the full burden. It discloses the isolated sandbox environment, timeout limit, and automatic payment handling, which are key behavioral traits. It could optionally mention error handling or return format, but it already provides significant transparency for a code execution 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 well-structured with a concise opening sentence, a short use-case paragraph, and a bulleted guideline list. Every sentence earns its place; there is no redundancy or 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?

  For a code execution tool with a single parameter and output schema present, the description covers purpose, environment, timeouts, imports, and payment behavior. It omits return-value details but the presence of an output schema reasonably handles that. It is complete enough for an agent to select and invoke correctly.

  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 description covers 100% of the single parameter, so baseline is 3. The description reinforces the need for self-contained code with imports included, but this is more of an instruction than new parameter semantics. It does not add syntax, format, or edge-case detail 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 the action ('Executes Python code'), the resource ('remote, isolated Azure sandbox environment'), and distinguishes it from sibling tools that target specific use cases like CSV sanitization or plot generation. It also lists concrete use cases, leaving no ambiguity about what the tool does.

  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?

  A dedicated 'Usage Guidelines' section provides explicit context for when to use the tool (evaluate algorithms, process data, math, custom scripts) and important constraints (standard Python 3, 30-second timeout, imports included, x402 payments). It does not name alternatives or exclusions, but the guidance is clear and actionable.

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

• optimize_ga_securelyA
  4.4/5.0
  Behavior4/5

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

  With no annotations provided, the description carries the behavioral transparency burden. It discloses the sandboxed secure execution environment, timeout risks, and performance/compute trade-offs via the given constraints. It does not describe error behavior or output details, but the output schema covers return structure.

  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 concise and well-structured: a one-sentence purpose, a short ideal-use case, and a bulleted list of guidelines. Every sentence contributes value with no redundancy or 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?

  The tool is moderately complex, with 3 params, an output schema, and security/sandbox context. The description covers purpose, use case, and key constraints, making it fairly complete. It does not detail the GA internals or failure modes, but these are non-essential given the schema and scope.

  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?

  While the input schema already covers all parameters (100% coverage), the description adds meaningful constraints beyond the schema: actuals size limit (5000), generations upper bound (1000), and confirmation of strict model_type enum. This enriches the agent's understanding of practical invocation limits.

  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 'Executes a Genetic Algorithm in a secure remote Azure sandbox to minimize Mean Absolute Percentage Error (MAPE)' against ground-truth targets. This specific verb-resource pair clearly distinguishes it from sibling tools like 'execute_code_securely' or 'generate_plot_securely'.

  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 provides a clear use case ('Ideal for driving down error metrics in complex time-series predictions') and specific operational limits (e.g., actuals ≤ 5000, generations < 1000). However, it does not explicitly mention alternative tools or exclusion scenarios, missing the '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.

• sanitize_csv_securelyA
  4.5/5.0
  Behavior4/5

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

  No annotations are present, so the description carries the full burden. It discloses specific cleaning behaviors ('strips leading/trailing whitespace, standardizes delimiters to commas, and resolves malformed rows') and sandbox-related constraints ('secure remote Azure sandbox environment', 'sandbox memory limits and payload timeouts'). This gives good insight into side effects and limitations, though it doesn't detail the output format (covered by output schema).

  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 efficiently structured: a clear opening sentence, a one-sentence purpose, and a bulleted list of usage guidelines. No redundant information; every sentence adds value.

  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 (one parameter), a complete schema, and an output schema, the description covers all necessary aspects: purpose, when to use, constraints, and behavioral specifics. It is a well-rounded description for this tool's complexity.

  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 description coverage is 100% for csv_content, so baseline is 3. The description adds meaning by specifying that csv_content 'must be a plain-text string representation of a CSV' and reiterates the text-only requirement, which supplements the schema description 'raw, unformatted CSV text string that requires cleaning.'

  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?

  Description clearly identifies the tool's function: 'Sanitize raw CSV data by cleaning and normalizing it within a secure remote Azure sandbox environment.' This uses a specific verb+resource (sanitize CSV) and the 'Use this tool to prepare messy tabular data' phrase further clarifies intent. It is distinct from sibling tools like execute_code_securely or generate_plot_securely.

  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?

  Provides explicit usage context: 'Use this tool to prepare messy tabular data for downstream processing' and includes concrete when-not constraints like 'Do not pass binary files or Excel (.xlsx) formats; strictly text-based CSV data.' However, it does not name alternative tools for cases where a different tool would be appropriate, so it falls short of a 5.

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