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detect_code_switching

Identify Hinglish/Tanglish code-switching patterns and calculate language mix ratios to analyze bilingual text composition.

Instructions

Detect Hinglish/Tanglish code-switching with language mix ratios. Cost: $0.003 USDC. Service: indic.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
primary_languageNo

Implementation Reference

  • This is a dynamic MCP server. The tool 'detect_code_switching' is not hardcoded but is fetched from a remote registry. The tool call handler resolves any requested tool name by looking it up in the fetched registry and proxying the request to the tool's defined endpoint.
    server.setRequestHandler(CallToolRequestSchema, async (request) => {
      const { name, arguments: args } = request.params;
    
      let registry: Registry;
      try {
        registry = await fetchRegistry();
      } catch (error) {
        return {
          content: [
            {
              type: "text",
              text: JSON.stringify({ error: "Failed to fetch tool registry", detail: String(error) }),
            },
          ],
        };
      }
    
      const tool = registry.tools.find((t) => t.name === name);
      if (!tool) {
        return {
          content: [
            {
              type: "text",
              text: JSON.stringify({
                error: `Tool '${name}' not found`,
                available_tools: registry.tools.map((t) => t.name),
              }),
            },
          ],
        };
      }
    
      try {
        const result = await callTool(tool, args as Record<string, unknown>);
        return {
          content: [
            {
              type: "text",
              text: JSON.stringify(result, null, 2),
            },
          ],
        };
      } catch (error) {
        return {
          content: [
            {
              type: "text",
              text: JSON.stringify({
                error: "Tool call failed",
                tool: name,
                service: tool.service,
                detail: String(error),
              }),
            },
          ],
        };
      }
    });
Behavior3/5

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 adds useful context: the cost ($0.003 USDC) and service type ('indic'), which are not in the schema. However, it doesn't describe output format, error handling, rate limits, or performance characteristics, leaving gaps for a tool with no 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded with the core purpose. The cost and service details are relevant but could be structured more clearly. It avoids redundancy, though it could benefit from better organization (e.g., separating functional and operational details).

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 (code-switching detection with 2 parameters), no annotations, 0% schema coverage, and no output schema, the description is incomplete. It lacks details on parameter usage, output format, error cases, and how language mix ratios are presented, making it inadequate for reliable tool invocation.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate for undocumented parameters. It doesn't explain the 'text' parameter (e.g., expected input format or length) or 'primary_language' (e.g., what values are valid or its role in detection). The description adds no parameter-specific information beyond what's in the schema.

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

Purpose4/5

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

The description clearly states the tool's purpose: detecting Hinglish/Tanglish code-switching with language mix ratios. It specifies the verb 'detect' and the resource 'code-switching', and mentions the specific language varieties (Hinglish/Tanglish). However, it doesn't explicitly differentiate from sibling tools like 'indic_sentiment' or 'transliterate', which might also process Indic language text.

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 minimal usage guidance: it mentions the service 'indic', implying it's for Indic language contexts, but doesn't specify when to use this tool versus alternatives like 'indic_sentiment' or 'transliterate'. No explicit when/when-not instructions or prerequisites are given.

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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