mcp-typescript server on vercel

endpoint

https://developers.llamaindex.ai/mcp

protocol

http-sse ·2025-06-18

authentication

none observed

public key

none — nobody has proven they own this listing · is it yours? claim it

karma

0 · newcomer

Is mcp-typescript server on vercel live?

Yes — it answered the hub's last check (checked 43m ago). It answered 100% of checks over the last 30 days.

Is mcp-typescript server on vercel free to use?

Yes — the hub reached it with no key and no payment.

What tools does mcp-typescript server on vercel have?

4 tools: grep_docs, read_doc, search_docs, list_docs.

Is mcp-typescript server on vercel safe to connect?

The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

• grep_docs unknown never probed

  Search for exact text or regex patterns across documentation files. Returns all matching lines with their file locations and optional surrounding context. Best for: finding specific class/function names, locating exact error messages or code snippets, discovering all occurrences of a term, or pattern matching with regex. Unlike search_docs which ranks by relevance, this returns ALL matches in order. Use when you know precise terminology or need exhaustive results. Can return up to 500 matches vs search_docs' 20 result limit.

  mcp-tool

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  {
    "type": "object",
    "$schema": "http://json-schema.org/draft-07/schema#",
    "required": [
      "pattern",
      "section"
    ],
    "properties": {
      "context": {
        "type": "integer",
        "default": 0,
        "maximum": 10,
        "minimum": 0,
        "description": "Number of context lines to show before and after each match (default: 0)"
      },
      "pattern": {
        "type": "string",
        "description": "Regular expression pattern to search for"
      },
      "section": {
        "enum": [
          "all",
          "llama-cloud",
          "liteparse",
          "llama-index-workflows",
          "llama-index",
          "for-agents",
          "llama-cloud-reference/http",
          "llama-cloud-reference/python",
          "llama-cloud-reference/typescript"
        ],
        "type": "string",
        "description": "Filter docs to a specific section, or 'all' for the whole site.\n- all: every section below\n- llama-cloud: LlamaParse product guides and examples (document parsing, extraction, classification, index). API reference lives under the llama-cloud-reference/* sections.\n- liteparse: LiteParse — local PDF/DOCX/image parsing CLI and library, no cloud dependency.\n- llama-index-workflows: LlamaAgents — the agent workflow engine and llamactl deployment tool.\n- llama-index: LlamaIndex framework — core Python LLM framework (indexes, retrievers, query engines) and integrations.\n- for-agents: Agent tooling overview — hosted MCP servers, agent skills and plugins, workflow nodes, and programmatic access to these docs.\n- llama-cloud-reference/http: LlamaCloud HTTP API reference (Stainless-generated): endpoints, request/response schemas.\n- llama-cloud-reference/python: LlamaCloud Python SDK reference (Stainless-generated): classes, methods, types.\n- llama-cloud-reference/typescript: LlamaCloud TypeScript SDK reference (Stainless-generated): classes, methods, types."
      },
      "maxResults": {
        "type": "integer",
        "default": 100,
        "maximum": 500,
        "minimum": 1,
        "description": "Maximum number of matches to return (default: 100, max: 500)"
      },
      "caseSensitive": {
        "type": "boolean",
        "default": false,
        "description": "Whether the search should be case-sensitive (default: false)"
      }
    },
    "additionalProperties": false
  }

  arguments 49 lines copy
• read_doc unknown 43m ago

  Read content from a specific documentation file by its path. By default returns the first 500 lines to avoid overwhelming context. Use startLine/endLine to read specific portions of longer documents. Accepts URL-style paths (e.g., '/python/framework/query-engine') or paths without extensions. Best for: reading documentation pages, understanding context after finding relevant excerpts, or accessing files you already know the path to.

  mcp-tool

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  {
    "type": "object",
    "$schema": "http://json-schema.org/draft-07/schema#",
    "required": [
      "path"
    ],
    "properties": {
      "path": {
        "type": "string",
        "description": "Path to the documentation file (e.g., '/python/framework/query-engine')"
      },
      "endLine": {
        "type": "integer",
        "minimum": 0,
        "description": "Ending line number (exclusive, default: 500). Use this to read beyond the first 500 lines."
      },
      "startLine": {
        "type": "integer",
        "minimum": 0,
        "description": "Starting line number (0-indexed, default: 0)"
      }
    },
    "additionalProperties": false
  }

  arguments 24 lines copy
• search_docs unknown 43m ago

  Search through llama-index documentation using BM25 ranked search. Best for conceptual queries, finding documentation by topic, or when you're not sure of exact terminology. Returns ranked results with excerpts showing match context. Use this when: exploring unfamiliar topics, looking for 'how to' guides, searching by concept rather than exact terms, or need to understand what documentation exists on a subject. For exact term matching or code patterns, use grep_docs instead.

  mcp-tool

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  {
    "type": "object",
    "$schema": "http://json-schema.org/draft-07/schema#",
    "required": [
      "query",
      "section"
    ],
    "properties": {
      "limit": {
        "type": "integer",
        "default": 10,
        "maximum": 20,
        "minimum": 1,
        "description": "Maximum number of results to return (default: 10, max: 20)"
      },
      "query": {
        "type": "string",
        "description": "The search query"
      },
      "section": {
        "enum": [
          "all",
          "llama-cloud",
          "liteparse",
          "llama-index-workflows",
          "llama-index",
          "for-agents",
          "llama-cloud-reference/http",
          "llama-cloud-reference/python",
          "llama-cloud-reference/typescript"
        ],
        "type": "string",
        "description": "Filter docs to a specific section, or 'all' for the whole site.\n- all: every section below\n- llama-cloud: LlamaParse product guides and examples (document parsing, extraction, classification, index). API reference lives under the llama-cloud-reference/* sections.\n- liteparse: LiteParse — local PDF/DOCX/image parsing CLI and library, no cloud dependency.\n- llama-index-workflows: LlamaAgents — the agent workflow engine and llamactl deployment tool.\n- llama-index: LlamaIndex framework — core Python LLM framework (indexes, retrievers, query engines) and integrations.\n- for-agents: Agent tooling overview — hosted MCP servers, agent skills and plugins, workflow nodes, and programmatic access to these docs.\n- llama-cloud-reference/http: LlamaCloud HTTP API reference (Stainless-generated): endpoints, request/response schemas.\n- llama-cloud-reference/python: LlamaCloud Python SDK reference (Stainless-generated): classes, methods, types.\n- llama-cloud-reference/typescript: LlamaCloud TypeScript SDK reference (Stainless-generated): classes, methods, types."
      },
      "fullContent": {
        "type": "boolean",
        "default": false,
        "description": "Include full document content in results (default: false, only excerpts)"
      }
    },
    "additionalProperties": false
  }

  arguments 42 lines copy
• list_docs unknown 43m ago

  Browse the documentation site structure to discover what pages and sections exist. Returns a tree showing directory slugs (usable as read_doc paths) and labels. Default depth is 1 (compact overview) when no section is specified, or 2 when a section/path is specified. Use depth=2-3 to see more detail. The path parameter performs a strict lookup — on a miss no tree is returned, just a short not-found message listing the searched sections; call again without path to browse.

  mcp-tool

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  {
    "type": "object",
    "$schema": "http://json-schema.org/draft-07/schema#",
    "required": [
      "section"
    ],
    "properties": {
      "path": {
        "type": "string",
        "description": "Strict lookup for a specific subtree path (e.g., 'llamaparse', 'module_guides/llms'). The path must match a directory or page path exactly (after normalization — leading/trailing slashes and case are ignored). On a miss, returns a one-line not-found message listing which sections were searched; no full-tree fallback."
      },
      "depth": {
        "type": "integer",
        "maximum": 5,
        "minimum": 1,
        "description": "How many levels of the tree to expand. Default: 1 for overview (no section), 2 when a section or path is specified. Groups beyond this depth are collapsed to show just their name and page count."
      },
      "section": {
        "enum": [
          "all",
          "llama-cloud",
          "liteparse",
          "llama-index-workflows",
          "llama-index",
          "for-agents",
          "llama-cloud-reference/http",
          "llama-cloud-reference/python",
          "llama-cloud-reference/typescript"
        ],
        "type": "string",
        "description": "Filter docs to a specific section, or 'all' for the whole site.\n- all: every section below\n- llama-cloud: LlamaParse product guides and examples (document parsing, extraction, classification, index). API reference lives under the llama-cloud-reference/* sections.\n- liteparse: LiteParse — local PDF/DOCX/image parsing CLI and library, no cloud dependency.\n- llama-index-workflows: LlamaAgents — the agent workflow engine and llamactl deployment tool.\n- llama-index: LlamaIndex framework — core Python LLM framework (indexes, retrievers, query engines) and integrations.\n- for-agents: Agent tooling overview — hosted MCP servers, agent skills and plugins, workflow nodes, and programmatic access to these docs.\n- llama-cloud-reference/http: LlamaCloud HTTP API reference (Stainless-generated): endpoints, request/response schemas.\n- llama-cloud-reference/python: LlamaCloud Python SDK reference (Stainless-generated): classes, methods, types.\n- llama-cloud-reference/typescript: LlamaCloud TypeScript SDK reference (Stainless-generated): classes, methods, types."
      }
    },
    "additionalProperties": false
  }

  arguments 35 lines copy

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• mcp.llamaindex.ai mcp.llamaindex.ai mcp · 0 tools