Server Quality Checklist

Tool Scores

• run_pandas_code_toolA
  3.9/5.0
  Behavior3/5

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

  Since no annotations are provided, the description carries the full burden. It discloses security restrictions and code formatting requirements but does not mention resource limits, execution timeouts, or sandboxing details, leaving some behavioral aspects unclear.

  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 well-structured with clear sections (Args, Returns, Forbidden Operations, Requirements) and each sentence adds value. It is not overly verbose, though the list of forbidden operations is lengthy but necessary for security.

  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 complexity as a code executor, the description covers security, required output format, and pre-imported pandas. It lacks details on execution limits or environment, but the essential context for safe usage is provided, making it fairly complete for a single-parameter tool.

  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?

  With 0% schema description coverage, the description must compensate. It explains the 'code' parameter as a Python string containing pandas operations and adds requirements, which is minimal but adequate. No further detail on expected code patterns or constraints beyond the listed forbidden operations.

  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 executes pandas code with smart suggestions and security checks, using a specific verb and resource. It is easily distinguished from siblings like generate_chartjs_tool, interpret_column_data, and read_metadata_tool, which serve different purposes.

  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 clear constraints on when to use the tool by listing forbidden operations and requirements, such as assigning the result to 'result' variable. However, it does not explicitly compare with sibling tools or state scenarios where alternatives would be preferred.

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

• generate_chartjs_toolA
  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 full burden. It discloses the tool generates interactive chart HTML, specifies the expected data structure, and describes the return format. It does not mention any destructive actions or authorization needs, which is acceptable for a read-like generation tool.

  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 organized into Args and Returns sections with an example, making it easy to parse. Though slightly lengthy, every sentence adds value for a complex input parameter. No redundant 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 lack of output schema and presence of nested objects, the description provides a thorough explanation of input structure, chart_types behavior, and return fields. It could mention error conditions or limitations, but overall it is sufficiently complete for an agent to use 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 description coverage is 0%, but the description compensates by detailing the required structure for 'data' with an example, explaining 'chart_types' behavior, and noting 'title' with a default. 'request_params' is vaguely described as 'additional visualization parameters', but overall adds significant meaning beyond the raw 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 explicitly states 'Generate interactive Chart.js visualizations from structured data', clearly identifying the tool's purpose. The sibling tools (interpret_column_data, read_metadata_tool, run_pandas_code_tool) are distinct, and no confusion arises.

  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 implies usage when structured data is available and a chart is needed. However, it does not explicitly state when not to use this tool or mention alternative tools, but the context is clear enough for an agent to infer appropriate usage.

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

• read_metadata_toolA
  4.3/5.0
  Behavior4/5

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

  No annotations are provided, but the description offers detailed return structures for both Excel and CSV, including status, file info, and column stats. It does not disclose potential side effects or limitations like file locking, but the output clarity is high.

  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 well-structured with Args and Returns sections, front-loading the purpose in the first sentence. It is slightly verbose but each part adds value, making it efficient for an agent.

  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 simplicity of the tool (one parameter, no output schema), the description provides a comprehensive overview of inputs and outputs, including file types, structure, and common columns. It is complete for an agent to use correctly.

  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?

  The input schema has one parameter (file_path) with no description, but the description adds significant meaning by specifying that it requires an absolute path to a data file. Since schema coverage is 0%, the description fully compensates.

  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 that the tool reads file metadata from Excel or CSV files and returns it in a structured MCP-compatible format. It distinguishes itself from siblings like generate_chartjs_tool (chart generation) and interpret_column_data (data interpretation) by focusing on metadata extraction.

  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 when metadata from Excel or CSV files is needed, but it does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or prerequisites.

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

• interpret_column_dataA
  4.8/5.0
  Behavior4/5

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

  No annotations provided, so description carries full burden. It details the return structure (status, file_info, columns_interpretation with counts, null_count, data_type). It could explicitly state that the tool is non-destructive, but overall it is transparent about behavior and output.

  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 sections for purpose, use cases, file types, args, returns. Each sentence adds value; no redundancy. Front-loaded with the main action. Appropriate length.

  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 and no annotations, the description is thorough: it includes return structure, use case guidance, file type support, and parameter descriptions. An agent can confidently use the tool without additional context.

  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 0%, but the description's Args section explains each parameter's meaning: file_path as absolute path, column_names as list to interpret, sheet_name with default. This adds significant context beyond the sparse 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 'Interpret column values and return their unique values,' which is a specific verb+resource. It distinguishes itself from sibling tools (chart generation, metadata reading, running pandas code) by focusing on column value interpretation.

  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 lists HIGH VALUE and LOW VALUE use cases with examples, and conditional cases. It guides the agent on when to use (categorical, code fields) and when not (ID, email, numeric), which is excellent for decision-making.

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