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seanshin0214

Dr. QuantMaster MCP Server

by seanshin0214

table_code

Generate statistical table code in R, Stata, or Python for descriptive, regression, correlation, and balance tables. Export to LaTeX, HTML, Word, or Markdown formats.

Instructions

결과표 생성 코드 (stargazer, esttab, pandas)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
table_typeYes표 유형
formatYes출력 형식
languageYes언어

Implementation Reference

  • The handler function that executes the table_code tool. It is a placeholder implementation that echoes the input parameters and provides generic advice on table generation using tools like modelsummary (R), esttab (Stata), or stargazer.
    function handleTableCode(args: Record<string, unknown>) {
      return {
        table_type: args.table_type,
        format: args.format,
        language: args.language,
        code: "Table generation code - use modelsummary (R), esttab (Stata), or stargazer"
      };
    }
  • Input schema for the table_code tool defining the expected parameters: table_type (enum: descriptive, regression, correlation, balance), format (enum: latex, html, word, markdown), and language (enum: r, stata, python). All are required.
    inputSchema: {
      type: "object",
      properties: {
        table_type: {
          type: "string",
          enum: ["descriptive", "regression", "correlation", "balance"],
          description: "표 유형"
        },
        format: { type: "string", enum: ["latex", "html", "word", "markdown"], description: "출력 형식" },
        language: { type: "string", enum: ["r", "stata", "python"], description: "언어" },
      },
      required: ["table_type", "format", "language"],
    },
  • Registration of the table_code tool in the tools array exported for MCP, including name, description, and reference to input schema.
      name: "table_code",
      description: "결과표 생성 코드 (stargazer, esttab, pandas)",
      inputSchema: {
        type: "object",
        properties: {
          table_type: {
            type: "string",
            enum: ["descriptive", "regression", "correlation", "balance"],
            description: "표 유형"
          },
          format: { type: "string", enum: ["latex", "html", "word", "markdown"], description: "출력 형식" },
          language: { type: "string", enum: ["r", "stata", "python"], description: "언어" },
        },
        required: ["table_type", "format", "language"],
      },
    },
  • Registration of the table_code handler in the main handleToolCall switch statement, mapping the tool name to its handler function.
    case "table_code":
      return handleTableCode(args);
Behavior2/5

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

No annotations are provided, so the description must fully disclose behavioral traits. It mentions '결과표 생성 코드' (result table generation code), which implies a read-only code generation operation, but doesn't clarify if it creates files, outputs text, or has side effects like modifying data. There's no information on permissions, rate limits, or error handling. The description adds minimal behavioral context beyond the basic action, failing to compensate for the lack of annotations.

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 a single, efficient phrase in Korean, listing key libraries without unnecessary words. It's front-loaded with the core purpose. However, it could be more structured by explicitly stating the action (e.g., 'Generate code for statistical tables in R, Stata, or Python'). The brevity is appropriate but slightly under-specified for clarity.

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

Completeness2/5

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

Given the complexity (3 parameters with enums, no output schema, no annotations), the description is incomplete. It doesn't explain the tool's output (e.g., code snippets, file paths), behavioral aspects, or how it differs from siblings. For a code generation tool with specific parameter constraints, more context is needed to guide the agent effectively, such as example use cases or output format details.

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 input schema has 100% description coverage with clear enums for table_type, format, and language, documenting all parameters. The description adds no parameter semantics beyond what the schema provides—it doesn't explain what 'descriptive' vs. 'regression' tables entail or how the libraries map to languages. With high schema coverage, the baseline is 3, as the description doesn't enhance parameter understanding but doesn't detract either.

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

Purpose2/5

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

The description '결과표 생성 코드 (stargazer, esttab, pandas)' is in Korean and translates to 'result table generation code (stargazer, esttab, pandas)'. It states the tool generates code for result tables using specific libraries, which gives a basic purpose. However, it's vague about what 'result tables' entail (e.g., statistical tables, data summaries) and doesn't differentiate from siblings like 'visualization_code' or 'code_template', which might also involve code generation. The title is null, so the description carries the full burden but only provides a general concept without specific verb+resource clarity.

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 lists library names (stargazer, esttab, pandas) which imply usage for R, Stata, and Python code generation, but doesn't specify contexts like 'use for academic paper tables' or 'prefer over visualization_code for tabular outputs'. With many sibling tools like 'generate_python_code' or 'write_results_section', there's no explicit or implied differentiation, leaving the agent to guess based on parameter enums alone.

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

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