To build an AI agent with Sanity, connect an MCP-capable application to Sanity Context for scoped, read-only content access, then let your application provide the agent loop and user interface. Start by choosing between live GROQ queries and a pre-indexed Knowledge Base, verify retrieval against a known document, and add writes only through a separate write-capable path with deliberate permissions.
How the agent, Sanity, and MCP fit together
Sanity’s Content Lake stores content as structured documents. Instead of asking an agent to interpret a page dump, you can expose fields such as titles, authors, categories, and body content as addressable data. Sanity describes this structure as queryable and reusable across channels. The Content Lake supports GROQ and GraphQL queries; see Sanity’s Content Lake documentation and its platform overview.
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Sanity Context is the hosted context server in this design. Sanity describes it as a Model Context Protocol (MCP) server that gives agents structured, read-only access to content. It can expose a schema-aware view within the scope you configure, but it does not run the agent loop, generate the conversation behavior, supply your application UI, or write to the dataset. Your MCP-compatible harness or application handles those responsibilities. Read Sanity Context documentation for the service boundary and modes.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problems- Content Lake: stores the documents and schema that provide the source material.
- Sanity Context: makes an appropriately scoped, read-only content view available to an MCP client.
- Your application: runs the model and agent loop, chooses when to call tools, and presents the result to users.
Choose GROQ or Knowledge Base mode
The right mode depends on whether the agent needs live structured records or answers drawn from material collected and indexed in advance. Sanity’s Context overview describes both options; its Context reference covers the GROQ-mode search behavior.
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| Mode | Best fit | How content is accessed | Important consideration |
|---|---|---|---|
| GROQ | Questions about structured records, fields, and current dataset content | Queries the live dataset within the schema and document scope allowed by the MCP configuration | Search options include keyword, semantic, and hybrid search; keyword search uses exact tokens, with no fuzzy matching or stemming, so misspellings may return no result. |
| Knowledge Base | Answers based on material assembled from datasets, websites, and files | Uses knowledge indexed ahead of time | Confirm the indexed material and its freshness suit the task; it is not the same access model as querying the live dataset. |
For GROQ mode, Sanity documents keyword search ranked with BM25, semantic search over dataset embeddings, and hybrid search with selectable boosting. That gives you options for retrieval, not a guarantee that every query will find the intended document. If exact structured fields matter, make the question and retrieval strategy precise; if the answer needs to span a prepared set of sources, consider the Knowledge Base route.
What you need before connecting
Sanity’s Context quick start documents the setup prerequisites. For dataset-backed use, prepare:
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- A Sanity organization with Context enabled.
- A Sanity project containing the content you want the agent to read.
- A deployed schema for the dataset source. The quick start specifies Studio v5.1.0 or later for schema deployment.
- An MCP-capable application or coding-agent harness that will run the agent loop.
- An endpoint configuration and authentication appropriate to the client and the intended read scope.
Sanity’s quick start describes installing its Context skill and running a guided setup that inspects the project. It also shows a sample using createMCPClient from @ai-sdk/mcp, a Context MCP URL, and an organization token. These are documented setup examples, not a requirement to use that particular harness: the documentation also points to manual configuration. Follow the current quick start for exact endpoint and credential handling rather than guessing values.
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Connect the endpoint and verify read access
Keep the first pass read-only. Configure the MCP client in your application using the Context endpoint and credentials that the organization and client support, then ask the agent for a known item from the configured content scope. Sanity’s quick start suggests using the create-agent-with-sanity-context skill with a coding agent to guide setup; it is an optional setup aid, not the agent runtime itself.
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- Confirm the source. Identify a document and field you expect the agent to retrieve, and check that the document belongs to the project and dataset you intend to expose.
- Configure Context. Use the current endpoint and authentication instructions in Sanity’s quick start for your MCP client. Restrict access to the content needed for this use case.
- Ask a narrow test question. Request a known field or a fact explicitly present in the test document. Avoid beginning with a broad question that could be answered from general model knowledge.
- Check grounding. Compare the answer with the source document. If your client exposes tool calls or retrieved content, inspect those too; a plausible response alone does not establish that retrieval succeeded.
- Test a realistic query. Once the known-document check passes, try a representative user question and confirm that the returned content remains within the intended scope.
For a development assistant rather than an application’s runtime agent, Sanity separately documents npx sanity@latest mcp configure and Agent Toolkit skills and plugins. This configures an AI coding workflow; it should not be confused with configuring the runtime Context endpoint used by your own application. See Sanity’s AI tools setup documentation.
Scope access before adding capabilities
Read-only access is a useful first boundary: the agent can retrieve and reason over authorized content without being able to change it. Decide which project, dataset, documents, or other supported scope belongs in the agent’s view, and use credentials whose role and permissions match that scope. Do not assume that a model prompt is an access-control mechanism; enforce access through the configured service and credentials.
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If retrieval fails, check the endpoint URL, the authentication method and token type expected by the client, whether the schema is deployed, and whether the requested content falls inside the configured scope. Those checks correspond to the prerequisites and access model in Sanity’s quick start and Context reference. For keyword search, also check spelling: Sanity documents exact-token matching without fuzzy matching or stemming.
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Sanity Context is the read-only option; use a separately documented write-capable route if the agent must change content. Two Sanity-documented paths are the hosted Sanity MCP server and the Content Agent API. They have different interfaces and permission models, so choose one deliberately rather than treating Context as if it can write.
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Sanity MCP server
Sanity hosts its MCP server at https://mcp.sanity.io. Its documentation says the server follows Anthropic’s official MCP specification and works with MCP-compatible clients. It documents OAuth by default or token authentication, with operations governed by the token’s role and permissions. The available tools include development and editorial operations such as schema exploration, GROQ queries, project tasks, and content or document changes. Review Sanity’s MCP server documentation and grant only the permissions needed for the intended tasks.
Content Agent API
For a custom content agent, Sanity’s Content Agent API offers another path. Its content-agent npm package is a Vercel AI SDK provider with stateful multi-turn .agent() threads and stateless .prompt() calls. The API documentation shows read and write capabilities configured independently and scoped with filters. See Sanity’s Content Agent API documentation.
Sanity lists a deployed schema, an Editor-level or higher project token, an organization ID, Node.js 18 or later, and a Sanity Studio v5.1.0 or later opened at least once after deployment among the API prerequisites. It also states that calls consume AI credits and that read-only queries cost less than write operations; the documentation cited here does not provide a specific price to quote. Treat that consumption and the stronger impact of writes as part of the design: keep read and write capabilities separate where possible, scope filters carefully, and require an appropriate application-level confirmation for consequential changes.
Build a safe first version
A practical first release can answer questions about content without changing it. Begin with a narrowly scoped source, verify retrieval using known documents, and make the application’s tool behavior observable enough to diagnose whether an answer came from retrieved content. Only add a mutation path when the task actually requires it, with a credential and scope designed for that path.
Quick Recap
- Use GROQ mode when the application needs live, schema-backed dataset access; use Knowledge Base mode when the relevant material is prepared and indexed from multiple sources.
- Test a known document before evaluating broad or ambiguous user questions.
- Keep the agent’s content scope and credentials aligned with the users and tasks it serves.
- Use a separately configured write-capable service for mutations, and distinguish the agent’s proposed change from an authorized write.
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