MCP Server

Use the Grapevine Model Context Protocol server to bring Company Context to your Agents

Overview

The Grapevine MCP provides 2 categories of tools that can be used to query your Company Context:

Search Tools are the core tools, intended to be used by an agent to find specific documents that match a query. These tools are intended to be used in sequence, and execute quickly. They are modeled after the tools that applications like Claude Code or Cline use to navigate a codebase.

In addition to the Search Tools, we provide a pre-packaged Agent Tool that is adept at using the Search Tools to research your Company Context and answer arbitrary questions.

Configuration

Grapevine runs as a remote MCP server at:

https://mcp.getgrapevine.ai

For more information on how to setup your Grapevine tenant, see Getting Started. For more information on how you can authenticate with the MCP, see the Authentication page.

Available tools

Search tools

These tools provide direct access to search and retrieve documents from your organization’s knowledge base.

semantic_search

Search your organization’s internal context for conceptually similar content using AI embeddings. Use this when you need to find documents that are semantically related to your query, even if they don’t contain exact keyword matches.

Request

query · string · required

Natural language query to search for semantically similar content

limit · integer · default: 10

Maximum number of results to return (min: 1, max: 100)

filters · SearchFilters

Filters to narrow down results. See SearchFilters for complete structure and available options

Show properties

sources · string[]

List of document sources to filter by. See Document Sources for available values

date_from · string

Start date in YYYY-MM-DD format. Only return documents created on or after this date

date_to · string

End date in YYYY-MM-DD format. Only return documents created on or before this date

provenance · string

Source-specific filter to narrow results within a connector (e.g., Slack channel name, GitHub repository name, Linear team name). See Provenance Filters for details

document_id · string

Exact document ID to retrieve. When provided, all other filters are ignored and only this specific document is returned

keyword_search

Search your organization’s internal context for documents containing specific keywords or phrases. Supports both natural language queries and advanced OpenSearch syntax.

Request

query · string · required

Search query

• Standard mode: Natural language keywords
• Advanced mode: OpenSearch syntax with operators (AND, OR, NOT, +, -, wildcards *, field queries field:value, grouping with parentheses, fuzzy search ~, boosting ^)

limit · integer · default: 10

Maximum number of results to return (min: 1, max: 100)

filters · SearchFilters

Filters to narrow down results. See SearchFilters for complete structure and available options

Show properties

sources · string[]

List of document sources to filter by. See Document Sources for available values

date_from · string

Start date in YYYY-MM-DD format. Only return documents created on or after this date

date_to · string

End date in YYYY-MM-DD format. Only return documents created on or before this date

provenance · string

Source-specific filter to narrow results within a connector (e.g., Slack channel name, GitHub repository name, Linear team name). See Provenance Filters for details

document_id · string

Exact document ID to retrieve. When provided, all other filters are ignored and only this specific document is returned

advanced · boolean · default: false

Enable advanced OpenSearch query syntax

get_document

Retrieve the full raw text content of a specific document from your organization’s internal context. Use this when you have a document ID from search results and need the complete document text.

Request

document_id · string · required

Document identifier. Format varies by connector. See Document Sources and IDs for details

get_document_metadata

Retrieve metadata for a document without downloading its full text content. Use this when you only need document information like source, dates, and metadata fields.

Request

document_id · string · required

Document identifier. Format varies by connector. See Document Sources and IDs for details

For complete details on document sources, ID formats, filters, and data structures, see the API Reference.

Agent Tools

ask_agent

This is the simplest way to get a comprehensive answer with citations - it is a good starting point for complex queries that require reasoning and synthesis across multiple documents.

ask_agent is akin to a Deep Research Agent - it is slow, thorough, and will answer with citations to the documents that were used to answer the question. Make sure your client is ready for large (100k+ tokens) and slow (multi-minute) responses.

Request

query · string · required

User question to answer with agentic search

files · array

Optional file attachments as list of objects with:

• name (string) - Filename
• mimetype (string) - MIME type
• content (string) - Base64 encoded file content

previous_response_id · string

Prior response ID to continue a conversation

output_format · string

Output format: use 'slack' for Slack markdown formatting

agent_prompt_override · string

Override the default system prompt

reasoning_effort · string · default: "medium"

OpenAI reasoning effort level: 'minimal', 'low', 'medium', or 'high'

verbosity · string

Response detail level: 'low', 'medium', or 'high'

ask_agent_streaming

Streaming variant of ask_agent that returns events as they occur. Use this when you need real-time progress updates during agentic search.

Request

Same as ask_agent above.