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code-alchemist01

Development Tools MCP Server

parse_csv

Parse CSV strings into structured data for development workflows. Specify delimiters to handle various CSV formats and extract usable information from raw data.

Instructions

Parse CSV data

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesCSV string to parse
delimiterNoCSV delimiter,

Implementation Reference

  • The handler function for the 'parse_csv' tool. It parses a CSV string into an array of objects, using the first row as headers and the specified delimiter.
    case 'parse_csv': {
      const data = params.data as string;
      const delimiter = (params.delimiter as string) || ',';
      const lines = data.split('\n');
      const headers = lines[0].split(delimiter);
      const rows = lines.slice(1).map((line) => {
        const values = line.split(delimiter);
        const obj: Record<string, string> = {};
        headers.forEach((header, index) => {
          obj[header.trim()] = values[index]?.trim() || '';
        });
        return obj;
      });
      return rows;
    }
  • Registration of the 'parse_csv' tool in the apiDiscoveryTools array, including its name, description, and input schema.
    {
      name: 'parse_csv',
      description: 'Parse CSV data',
      inputSchema: {
        type: 'object',
        properties: {
          data: {
            type: 'string',
            description: 'CSV string to parse',
          },
          delimiter: {
            type: 'string',
            description: 'CSV delimiter',
            default: ',',
          },
        },
        required: ['data'],
      },
    },
    {
  • Input schema definition for the 'parse_csv' tool, specifying the expected parameters.
    {
      name: 'parse_csv',
      description: 'Parse CSV data',
      inputSchema: {
        type: 'object',
        properties: {
          data: {
            type: 'string',
            description: 'CSV string to parse',
          },
          delimiter: {
            type: 'string',
            description: 'CSV delimiter',
            default: ',',
          },
        },
        required: ['data'],
      },
    },
    {
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Parse CSV data' implies a read-only transformation, but it doesn't specify output format (e.g., array of objects), error handling for malformed data, or performance considerations like size limits. This leaves significant gaps for a tool with no annotation coverage.

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 extremely concise at three words, with no wasted language. It's front-loaded with the core action, though this brevity contributes to gaps in other dimensions. Every word earns its place by directly stating the tool's function.

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 no annotations and no output schema, the description is incomplete for a data parsing tool. It doesn't explain the return value (e.g., parsed structure), error cases, or usage context compared to siblings. While the schema covers inputs well, the overall tool behavior remains underspecified for effective agent use.

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%, with clear parameter descriptions in the schema (e.g., 'CSV string to parse' for data, 'CSV delimiter' for delimiter). The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline for high schema coverage without compensating value.

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

Purpose3/5

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

The description 'Parse CSV data' clearly states the verb (parse) and resource (CSV data), making the basic purpose understandable. However, it lacks specificity about what parsing entails (e.g., converting to structured format) and doesn't differentiate from sibling tools like parse_json or parse_xml, which perform similar parsing operations on different data formats.

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 doesn't mention sibling tools like parse_json or parse_xml for different data formats, or tools like extract_tables that might handle tabular data extraction. There's no context about prerequisites, such as requiring raw CSV text as input.

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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