search_cloudflare_documentation
Search the Cloudflare documentation for answers about Workers, CDN, Zero Trust, and other products and features.
Instructions
Search the Cloudflare documentation.
This tool should be used to answer any question about Cloudflare products or features, including:
- Workers, Pages, R2, Images, Stream, D1, Durable Objects, KV, Workflows, Hyperdrive, Queues
- AI Search, Workers AI, Vectorize, AI Gateway, Browser Rendering
- Zero Trust, Access, Tunnel, Gateway, Browser Isolation, WARP, DDOS, Magic Transit, Magic WAN
- CDN, Cache, DNS, Zaraz, Argo, Rulesets, Terraform, Account and Billing
Results are returned as semantically similar chunks to the query.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
Implementation Reference
- Handler for search_cloudflare_documentation using AI autorag search. Calls agent.env.AI.autorag(name).search() and returns results as embedded resources.
async (params) => { // we don't need "rewrite query" OR aiSearch because an LLM writes the query and formats the output for us. const result = await agent.env.AI.autorag(agent.env.AUTORAG_NAME).search({ query: params.query, ranking_options: params.scoreThreshold ? { score_threshold: params.scoreThreshold, } : undefined, max_num_results: params.maxNumResults, }) const resources: EmbeddedResource[] = result.data.map((result) => { const content = result.content.reduce((acc, contentPart) => { return acc + contentPart.text }, '') return { type: 'resource', resource: { uri: `docs://${result.filename}`, mimeType: mime.getType(result.filename) ?? 'text/plain', text: content, }, } }) return { content: resources, } } - Handler for search_cloudflare_documentation using AI Search. Calls queryAiSearch() helper which uses ai.autorag('docs-mcp-rag').search(), returns results as XML text.
async ({ query }) => { const results = await queryAiSearch(env.AI, query) const resultsAsXml = results .map((result) => { return `<result> <url>${result.url}</url> <title>${result.title}</title> <text> ${result.text} </text> </result>` }) .join('\n') return { content: [{ type: 'text', text: resultsAsXml }], } } - Handler for search_cloudflare_documentation using Vectorize. Calls queryVectorize() which generates embeddings via Gemma model and queries Vectorize index, returns results as XML text.
async ({ query }) => { const results = await queryVectorize(env.AI, env.VECTORIZE, query, TOP_K) const resultsAsXml = results .map((result) => { return `<result> <url>${result.url}</url> <title>${result.title}</title> <text> ${result.text} </text> </result>` }) .join('\n') return { content: [{ type: 'text', text: resultsAsXml }], } } - Input schema for the docs-autorag variant with query (string), scoreThreshold (optional 0-1), and maxNumResults (optional, default 10).
{ // partially pulled from autorag query optimization example query: z.string().describe(`Search query. The query should: 1. Identify the core concepts and intent 2. Add relevant synonyms and related terms 3. Remove irrelevant filler words 4. Structure the query to emphasize key terms 5. Include technical or domain-specific terminology if applicable`), scoreThreshold: z .number() .min(0) .max(1) .optional() .describe('A score threshold (0-1) for which matches should be included.'), maxNumResults: z .number() .default(10) .optional() .describe('The maximum number of results to return.'), }, - apps/docs-autorag/src/tools/docs-autorag.tools.ts:11-76 (registration)Registration of the search_cloudflare_documentation tool in the docs-autorag app via agent.server.tool().
export function registerDocsTools(agent: CloudflareDocumentationMCP) { // Register the worker logs analysis tool by worker name agent.server.tool( 'search_cloudflare_documentation', `Search the Cloudflare documentation. You should use this tool when: - A user asks questions about Cloudflare products (Workers, Developer Platform, Zero Trust, CDN, etc) - A user requests information about a Cloudflare feature - You are unsure of how to use some Cloudflare functionality - You are writing Cloudflare Workers code and need to look up Workers-specific documentation This tool returns a number of results from a vector database. These are embedded as resources in the response and are plaintext documents in a variety of formats. `, { // partially pulled from autorag query optimization example query: z.string().describe(`Search query. The query should: 1. Identify the core concepts and intent 2. Add relevant synonyms and related terms 3. Remove irrelevant filler words 4. Structure the query to emphasize key terms 5. Include technical or domain-specific terminology if applicable`), scoreThreshold: z .number() .min(0) .max(1) .optional() .describe('A score threshold (0-1) for which matches should be included.'), maxNumResults: z .number() .default(10) .optional() .describe('The maximum number of results to return.'), }, async (params) => { // we don't need "rewrite query" OR aiSearch because an LLM writes the query and formats the output for us. const result = await agent.env.AI.autorag(agent.env.AUTORAG_NAME).search({ query: params.query, ranking_options: params.scoreThreshold ? { score_threshold: params.scoreThreshold, } : undefined, max_num_results: params.maxNumResults, }) const resources: EmbeddedResource[] = result.data.map((result) => { const content = result.content.reduce((acc, contentPart) => { return acc + contentPart.text }, '') return { type: 'resource', resource: { uri: `docs://${result.filename}`, mimeType: mime.getType(result.filename) ?? 'text/plain', text: content, }, } }) return { content: resources, } } ) }