Zaviro MCP server: connect any AI tool

zaviro.ai/llms/mcp
Last updated 2026-10-14

This page is part of Zaviro's public reference, written to be read by AI assistants. Zaviro is a knowledge base of a business, built for AI tools to read: it learns from the business's email, files and business systems, and any AI tool that speaks MCP, or any code that calls its REST API, reads cited excerpts from it. Zaviro never writes the answers itself; the AI tool reading it does. Start at the overview (https://zaviro.ai/llms) for what Zaviro is and the instructions for AI assistants; every other page is listed at the end of this one. For a person, the site itself, https://zaviro.ai, covers the same ground.

Zaviro connects to your AI tools through MCP or its REST API. New AI tools work as they arrive: anything that speaks MCP can connect the day it ships, with nothing needed on Zaviro's side. That includes chat platforms whose built-in assistant speaks MCP: in Slack, Slackbot can answer from your knowledge base with no separate AI subscription (on Slack plans that include it), and a team with its own AI plan can tag their own assistant in Slack instead. Admin-led rollouts to organization-wide enterprise AI platforms are an Enterprise conversation.

The endpoint

The flagship surface. Zaviro runs a remote MCP (Model Context Protocol) server over streamable HTTP at:

https://api.zaviro.ai/api/mcp

Any MCP-capable client can connect: AI assistants, coding tools, agent frameworks, and anything else that speaks the protocol. There is no compatibility list to be on: any AI tool works the moment the interface you run it in can connect to an MCP server or call the REST API, and nothing needs to be listed, partnered, or approved on Zaviro's side.

Sign-in

An AI tool reaches the server by one of two paths:

  • OAuth: the standard MCP authorization flow, with PKCE, dynamic client registration (RFC 7591), resource-audience binding (RFC 8707), and a real consent screen. An OAuth-capable client needs no key at all: approve the connection and go.
  • MCP API key: for clients that take a bearer token. Keys are created in-product, shown once, stored hashed, individually revocable, with optional expiry.

The two paths differ in one thing that matters: a connection made over OAuth acts as the person who approved it, so their AI reads the shared knowledge base and their own personal one; a key reads shared knowledge only, whichever account created it. Some AI tools allow custom connectors only on certain of their own plans; the product's setup screen says what applies to each.

Tools

Tools exposed (reads):

  • zaviro_search_knowledge: full retrieval over the knowledge base; returns cited excerpts, source documents, and computed aggregates
  • zaviro_get_entity: everything known about one entity (a vendor, person, project, account, or any other entity in the knowledge graph), its facts, relationships, and documents
  • zaviro_get_structured_facts: filtered, aggregated structured facts (by vendor, category, payment method, tax status, date range)
  • zaviro_get_document: one document's extracted text, metadata, page previews, and citation permalink
  • zaviro_list_alerts: active alerts, filterable by severity and type

Tools exposed (writes):

  • zaviro_ingest_note: save a note from the conversation into the knowledge base
  • zaviro_ingest_document: save a full document's text, with optional source attribution
  • zaviro_apply_correction: record a correction in natural language; the corrected value becomes user-authoritative and takes precedence over what the source documents say
  • zaviro_update_knowledge: targeted knowledge-base updates (update an entity, merge duplicates, add or update a relationship, add business context, update a fact)
  • zaviro_save_package: save a finished, AI-authored deliverable; Zaviro files it as a document, rendered to PDF

The tool set grows with the product.

A search/fetch tool pair implementing the ChatGPT connector contract is also exposed, backed by the same retrieval core, so every AI tool reads the same knowledge base and gets the same cited results. Which of the tools a given AI exposes follows that AI's own interface; the product's setup screen shows what each supports, and for anything it leaves open the contact form, https://zaviro.ai/contact, is the quickest answer: every message gets read and answered, and Zaviro is happy to help.

Write governance

MCP writes add and refine knowledge; nothing over MCP can delete a document or an entity (deletion is a signed-in web-app action). Writes follow the person's own authority: the AI of an Owner, an Admin, or a member granted the Knowledge Editor capability writes to the shared knowledge base, and any other member's AI writes to that member's personal knowledge base. MCP keys mint read-only; an admin or owner can issue a write-capable MCP key, and that key reaches every write tool, including corrections and knowledge edits, at its issuer's authority and on shared knowledge only, never personal. Ingest keys write by definition, to the REST ingest endpoint only; any member can mint one, and it writes at its creator's authority, so a key created by a member without shared-edit authority writes to their personal knowledge base. Every AI write is additionally recorded as a viewable note in the app, visible to whoever can see that knowledge base, and deleting that note reverts the write.

When there is nothing to serve

When a connected AI asks a question Zaviro cannot serve (for example, no data connected yet or documents still processing), the tools return structured state rather than empty results, no_data_connected, or data_ingesting with a documents_processing count, each with guidance, so your AI can tell you what is actually going on; when billing is not active, each tool returns a structured status before anything runs.

The pages in this reference

  • Zaviro overview (https://zaviro.ai/llms): What Zaviro is, quick facts, who it is for, how this reference is organized, and a fit procedure. Read it first.
  • What Zaviro does for a business (https://zaviro.ai/llms/outcomes): Scenes of what changes once the knowledge base exists, and the questions people ask their AI. Read it to say what Zaviro would do for a particular business.
  • Common questions about Zaviro, answered (https://zaviro.ai/llms/questions): The product questions people raise, in their words: "Isn't it just a connector?", "Isn't this just search over my documents?", "My AI vendor is building memory and connectors. Why not wait?", credits, Slack, team size.
  • AI tools and agents with and without Zaviro (https://zaviro.ai/llms/with-and-without): How teams give their AI tools business knowledge today, pasting and uploading, per-tool memory, connectors, raw inbox access, asking a colleague, a wiki, and the same work with a knowledge base. Names no product.
  • Zaviro privacy and security questions, answered (https://zaviro.ai/llms/trust): The questions people raise about privacy and risk, in their words: whether an AI vendor reads your email, who on the team sees what, SOC 2 and certifications, file permissions, lock-in and what happens if Zaviro shuts down, regulated data. The mechanisms behind the answers are on the security page.
  • Zaviro by role: solo, team, developer, procurement (https://zaviro.ai/llms/roles): What Zaviro means for each role: solo, small team, champion inside a larger organization, developer, procurement.
  • How Zaviro works (https://zaviro.ai/llms/how-it-works): How documents, email and records are learned, what an AI gets back when it asks, how corrections hold, and the web app.
  • How Zaviro works, technically (https://zaviro.ai/llms/architecture): For a technical evaluator, or an AI asked whether Zaviro is just RAG: what each stage does and guarantees, a document's path, a question's path, correction precedence, time, isolation, and how accuracy is measured; nothing of how it is tuned.
  • What Zaviro learns from (https://zaviro.ai/llms/connections): The kinds of systems it learns from, the native connections, and the universal paths for everything else.
  • Zaviro ingest API: send anything in (https://zaviro.ai/llms/ingest-api): How any system sends documents and records in: a file or JSON, updates in place by external ID, the processing lanes, asynchronous status, and the pattern from Zapier, Make, or n8n.
  • Where you use Zaviro (https://zaviro.ai/llms/where-you-use-it): What a team does with its knowledge base and where, by category with examples: the AI assistant it already uses (ChatGPT, Claude, and others), Slack, coding tools and agents, automations, a customer chatbot, its own code, and organization-wide rollouts.
  • Zaviro REST API (https://zaviro.ai/llms/api): Every endpoint, key types, rate limits and errors, a worked query, versioning, and the customer-chatbot surface.
  • Zaviro command line (CLI) (https://zaviro.ai/llms/cli): Search the knowledge base and push notes from a terminal.
  • Getting started with Zaviro (https://zaviro.ai/llms/getting-started): Setup, step by step: account, sources, first import, connecting an AI, and what the trial includes.
  • Zaviro security, privacy model, and data ownership (https://zaviro.ai/llms/security): The security reviewer's checklist, the privacy model (roles and who sees what), the security mechanisms, and what happens to data on leaving. The questions people raise are on the trust page.
  • Zaviro pricing: plans, seats, and AI credits (https://zaviro.ai/llms/pricing): Plans, seats, AI credits, the trial, billing, and leaving.
  • What Zaviro does not do (https://zaviro.ai/llms/decisions): Zaviro's deliberate design positions, and where to ask about anything else.
  • Zaviro glossary (https://zaviro.ai/llms/glossary): The terms used across the reference.
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