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piyushgIITian

GitHub Enterprise MCP Server

search-code

Search code across GitHub repositories using natural language queries to find specific functions, files, or code patterns within your organization's codebase.

Instructions

Search for code across GitHub repositories

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
orderNo
pageNo
per_pageNo
qYes

Implementation Reference

  • The main handler function that performs GitHub code search using Octokit API, validates input with SearchCodeSchema, processes and returns search results.
    export async function searchCode(args: unknown): Promise<any> {
      const { q, order, page, per_page } = SearchCodeSchema.parse(args);
      const github = getGitHubApi();
    
      return tryCatchAsync(async () => {
        const { data } = await github.getOctokit().search.code({
          q,
          order,
          page,
          per_page,
        });
    
        return {
          total_count: data.total_count,
          incomplete_results: data.incomplete_results,
          items: data.items.map((item) => ({
            name: item.name,
            path: item.path,
            sha: item.sha,
            url: item.html_url,
            repository: item.repository ? {
              name: item.repository.name,
              full_name: item.repository.full_name,
              owner: {
                login: item.repository.owner.login,
              },
            } : null,
            score: item.score,
          })),
        };
      }, 'Failed to search code');
    }
  • Input schema definition for the 'search-code' tool advertised to MCP clients via ListToolsRequestSchema.
      name: 'search-code',
      description: 'Search for code across GitHub repositories',
      inputSchema: {
        type: 'object',
        properties: {
          q: {
            type: 'string',
          },
          order: {
            type: 'string',
            enum: ['asc', 'desc'],
          },
          page: {
            type: 'number',
            minimum: 1,
          },
          per_page: {
            type: 'number',
            minimum: 1,
            maximum: 100,
          },
        },
        required: ['q'],
        additionalProperties: false,
      },
    },
  • Switch case in MCP server's CallToolRequestSchema handler that dispatches to the searchCode implementation.
    case 'search-code':
      result = await searchCode(parsedArgs);
      break;
  • Zod schema used for runtime input validation inside the searchCode handler function.
    export const SearchCodeSchema = z.object({
      q: z.string().min(1, 'Search query is required'),
      order: z.enum(['asc', 'desc']).optional(),
      page: z.number().min(1).optional(),
      per_page: z.number().min(1).max(100).optional(),
    });
Behavior2/5

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

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It mentions 'search' but doesn't disclose rate limits, authentication needs, pagination behavior, or what the search encompasses (e.g., file types, repositories). This is inadequate for a tool with 4 parameters and 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.

Conciseness5/5

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

The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core purpose, making it easy to parse quickly.

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 4 parameters with 0% schema coverage, no annotations, and no output schema, the description is severely incomplete. It doesn't explain search syntax, result format, limitations, or error handling, leaving critical gaps for effective tool use.

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 but doesn't. It doesn't explain what 'q' (the required query parameter) should contain, how 'order' applies to results, or the purpose of 'page' and 'per_page'. The description adds no meaning beyond the bare 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 action ('Search for code') and target ('across GitHub repositories'), which is specific and unambiguous. However, it doesn't differentiate from sibling tools like 'search-issues' or 'search-repositories' beyond the 'code' focus, so it's not fully distinguished from alternatives.

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 like 'search-issues' or 'search-repositories'. It doesn't mention prerequisites, context, or exclusions, leaving the agent to infer usage based on the name 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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