Skip to main content
Glama

reference-attributes

Access comprehensive documentation for Turbo data attributes and meta tags to control Drive behavior, frame navigation, and caching functionality.

Instructions

Complete reference for Turbo data attributes and meta tags - covers all data-turbo-* attributes for controlling Drive behavior, frame navigation, caching, and automatically added attributes like aria-busy

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Handler function that executes the tool logic for 'reference-attributes': reads the markdown content from 'reference/attributes.md' via readMarkdownFile and returns it as a text content block in MCP format, with error handling.
    async () => { try { const content = await readMarkdownFile(path.join(folder, file)); return { content: [ { type: "text", text: content } ] }; } catch (error) { const errorMessage = error instanceof Error ? error.message : String(error); return { content: [ { type: "text", text: `Error reading ${file}: ${errorMessage}` } ] }; } } );
  • src/index.ts:17-45 (registration)
    Registers the 'reference-attributes' tool (along with other doc tools) on the MCP server using server.tool(), with name and description from config, and the shared handler function.
    docFiles.forEach(({ folder, file, name, description }) => { server.tool( name, description, async () => { try { const content = await readMarkdownFile(path.join(folder, file)); return { content: [ { type: "text", text: content } ] }; } catch (error) { const errorMessage = error instanceof Error ? error.message : String(error); return { content: [ { type: "text", text: `Error reading ${file}: ${errorMessage}` } ] }; } } ); });
  • Configuration entry in docFiles array that defines the tool name 'reference-attributes', its corresponding doc file path 'reference/attributes.md', and description used in registration.
    folder: 'reference', file: 'attributes.md', name: 'reference-attributes', description: 'Complete reference for Turbo data attributes and meta tags - covers all data-turbo-* attributes for controlling Drive behavior, frame navigation, caching, and automatically added attributes like aria-busy' },
  • Supporting function called by the handler to fetch markdown content for 'reference/attributes.md' from cache, GitHub raw file, or local filesystem, with commit-based caching.
    export async function readMarkdownFile(filename: string): Promise<string> { const filePath = path.join(docsFolder, filename); if (!filePath.startsWith(docsFolder)) { throw new Error("Invalid file path"); } // Get current commit info if we don't have it yet if (!mainBranchInfo) { try { const commitInfo = await fetchMainBranchInformation(); const cacheKey = `${commitInfo.sha.substring(0, 7)}-${commitInfo.timestamp}`; mainBranchInfo = { ...commitInfo, cacheKey }; } catch (shaError) { console.error('Failed to get GitHub commit info, falling back to direct fetch'); } } // Try to read from cache first if we have commit info if (mainBranchInfo) { const cachedFilePath = path.join(cacheFolder, mainBranchInfo.cacheKey, filename); try { const content = await fs.promises.readFile(cachedFilePath, "utf-8"); console.error(`Using cached content for ${mainBranchInfo.cacheKey}: ${filename}`); return content; } catch (cacheError) { // Cache miss, continue to fetch from GitHub } } // Fetch from GitHub try { return await fetchFromGitHub(filename, mainBranchInfo?.cacheKey); } catch (githubError) { console.error(`GitHub fetch failed: ${githubError}, attempting to read from local files...`); // Fallback: read from local files try { return await fs.promises.readFile(filePath, "utf-8"); } catch (localError) { const githubErrorMessage = githubError instanceof Error ? githubError.message : String(githubError); const localErrorMessage = localError instanceof Error ? localError.message : String(localError); throw new Error(`Failed to read file from GitHub (${githubErrorMessage}) and locally (${localErrorMessage})`); } } }

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/pinzonjulian/turbo-docs-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server