Build an Agent with Sanity Context

Give AI agents intelligent access to your Sanity content. Unlike embedding-only approaches, Agent Context is schema-aware—agents can reason over your content structure, query with real field values, follow references, and combine structural filters with semantic search.

What this enables:

Agents understand the relationships between your content types
Queries use actual schema fields, not just text similarity
Results respect your content model (categories, tags, references)
Semantic search is available when needed, layered on structure

Agent Context gives agents your schema and teaches them GROQ, but it can't know your domain. You close that gap through the Instructions field (dataset-specific query guidance) and optionally the system prompt (agent behavior and tone).

Three actors in this workflow:

You — the agent executing this skill, helping the user set things up
The user — the human you're working with, who knows their domain and data
The production agent — the agent being built, which will serve end users

What You'll Need

Before starting, gather these credentials:

Credential	Where to get it
Sanity Project ID	Your `sanity.config.ts` or sanity.io/manage
Dataset name	Usually `production` — check your `sanity.config.ts`
Sanity API read token	Create at sanity.io/manage → Project → API → Tokens. See HTTP Auth docs
LLM API key	From your LLM provider (Anthropic, OpenAI, etc.) — any provider works

How Agent Context Works

An MCP server that gives AI agents structured access to Sanity content. The core integration pattern:

MCP Connection: HTTP transport to the Agent Context URL
Authentication: Bearer token using Sanity API read token
Tool Discovery: Get available tools from MCP client, pass to LLM
System Prompt: Tell the production agent its role, tone, and boundaries

MCP URL formats:

https://api.sanity.io/:apiVersion/agent-context/:projectId/:dataset — Access all content, no custom configuration
https://api.sanity.io/:apiVersion/agent-context/:projectId/:dataset/:slug — Use an Agent Context Document's configuration

Agent Context Documents (type sanity.agentContext) are created in Sanity Studio and configure the MCP endpoint. They have three fields:

Field	Schema field	Purpose
Slug	`slug`	Unique URL identifier — becomes the `:slug` in the MCP URL
Instructions	`instructions`	Domain-specific guidance for the agent, injected into tool descriptions
Content Filter	`groqFilter`	A GROQ expression scoping which documents the agent can access

This means Studio users can manage agent behavior without touching code — updating instructions or narrowing the content filter takes effect immediately.

URL query params override the document's configuration (useful for testing and development):

?instructions=<content> — Override instructions (use ?instructions="" for a blank slate)
?groqFilter=<expression> — Override the content filter

The integration is simple: Connect to the MCP URL, get tools, use them. The reference implementation shows one way to do this—adapt to your stack and LLM provider.

Available MCP Tools

Tool	Purpose
`initial_context`	Get compressed schema overview (types, fields, document counts)
`groq_query`	Execute GROQ queries with optional semantic search
`schema_explorer`	Get detailed schema for a specific document type

For development and debugging: The general Sanity MCP provides broader access to your Sanity project (schema deployment, document management, etc.). Useful during development but not intended for customer-facing applications.

Before You Start: Understand the User's Situation

A complete integration has three distinct components that may live in different places:

Component	What it is	Examples
1. Studio Setup	Configure the context plugin and create agent context documents	Sanity Studio (separate repo or embedded)
2. Agent Implementation	Code that connects to Agent Context and handles LLM interactions	Next.js API route, Express server, Python service, or any MCP-compatible client
3. Frontend (Optional)	UI for users to interact with the agent	Chat widget, search interface, CLI—or none for backend services

Studio setup and agent implementation are required. Frontend is optional—many agents run as backend services or integrate into existing UIs.

Ask the user which part they need help with:

Components in different repos (most common): You may only have access to one component. Complete what you can, then tell the user what steps remain for the other repos.
Co-located components: All three in the same project—work through them one at a time (Studio → Agent → Frontend).
Already on step 2 or 3: If you can't find a Studio in the codebase, ask the user if Studio setup is complete.

Also understand:

Their stack: What framework/runtime? (Next.js, Remix, Node server, Python, etc.)
Their AI library: Vercel AI SDK, LangChain, direct API calls, etc.
Their domain: What will the agent help with? (Shopping, docs, support, search, etc.)

The reference patterns use Next.js + Vercel AI SDK, but adapt to whatever the user is working with.

Workflow

Quick Validation (Optional)

Before building the production agent, validate that the MCP endpoint is reachable:

curl -X POST https://api.sanity.io/YOUR_API_VERSION/agent-context/YOUR_PROJECT_ID/YOUR_DATASET \
  -H "Authorization: Bearer $SANITY_API_READ_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc": "2.0", "method": "tools/list", "id": 1}'

This confirms the token works and the endpoint is reachable. The base URL (no slug) gives access to all content — use a slug-based URL in production to apply the Agent Context Document's filter and instructions.

Step 1: Set up Sanity Studio

Help the user configure the @sanity/agent-context plugin in their Studio and create an Agent Context Document. This document controls what the production agent can see (via groqFilter) and what guidance it receives (via instructions).

See references/studio-setup.md

Step 2: Build the Agent (Adapt to user's stack)

The user already has an agent or MCP client? They just need to connect it to their Agent Context URL with a Bearer token. The tools will appear automatically.

Building from scratch? Help the user set up the MCP connection and LLM integration. The reference implementations use Vercel AI SDK with Anthropic, but the pattern works with any LLM provider (OpenAI, local models, etc.). Start with the basics and add advanced patterns as needed.

Framework-specific guides:

Next.js: See references/nextjs-agent.md
SvelteKit: See references/sveltekit-agent.md
Other stacks (Express, Remix, Python, LangChain): See references/adapting-to-stacks.md

System prompts (applies to all frameworks): See references/system-prompts.md for structure and domain-specific examples (e-commerce, docs, support, content curation).

The framework guides cover:

Core setup (required): MCP connection, authentication, basic chat route
Frontend (optional): Chat component for the framework
Advanced patterns (optional): Client-side tools, auto-continuation, custom rendering

Step 3: Conversation Classification (Optional)

Track and analyze agent conversations using Sanity Functions. Useful for analytics, debugging, and understanding user interactions.

See references/conversation-classification.md.

Step 4: Tune Your Agent (Recommended)

Once the production agent works:

Tune the Instructions field using the dial-your-context skill — an interactive session where you explore the user's dataset together, verify findings, and produce concise Instructions that teach the production agent what the schema alone doesn't make obvious: counter-intuitive field names, second-order reference chains, data quality issues, required filters, and query patterns. The skill can also help configure a groqFilter to scope what content the production agent sees.
Shape the system prompt (optional) using the shape-your-agent skill — if the user controls the production agent's system prompt, this helps define tone, boundaries, and guardrails. Skip this if the user doesn't control the system prompt.

GROQ with Semantic Search

Agent Context supports text::semanticSimilarity() for semantic ranking:

*[_type == "article" && category == "guides"]
  | score(text::semanticSimilarity("getting started tutorial"))
  | order(_score desc)
  { _id, title, summary }[0...10]

Always use order(_score desc) when using score() to get best matches first.

Adapting to Different Stacks

The MCP connection pattern is framework and LLM-agnostic. Whether Next.js, Remix, Express, or Python FastAPI—the HTTP transport works the same. Any LLM provider that supports tool calling will work.

See references/adapting-to-stacks.md for:

Framework-specific route patterns (Express, Remix, Python)
AI library integrations (LangChain, direct API calls)

See references/system-prompts.md for domain-specific examples (e-commerce, docs, support, content curation).

Best Practices

Start simple: Build the basic integration first, then add advanced patterns as needed
Schema design: Use descriptive field names—agents rely on schema understanding
GROQ queries: Always include _id in projections so agents can reference documents
Content filters: Use groqFilter to scope what the production agent sees — start broad, then narrow based on what it actually needs. The filter is a full GROQ expression (e.g., _type in ["product", "article"])
Instructions field: Keep it concise — only include what the auto-generated schema doesn't make obvious. Don't duplicate schema information. See the dial-your-context skill.
System prompts: Be explicit about forbidden behaviors and formatting rules. Less is more — an over-engineered prompt can interfere with the Instructions content. See the shape-your-agent skill.
Package versions: NEVER guess package versions. Always check the reference package.json files or use npm info <package> version. AI SDK and Sanity packages update frequently—outdated versions will cause errors.

Troubleshooting

Agent Context returns errors or no schema

Agent Context requires a deployed Studio. See Deploy Your Studio for instructions.

"401 Unauthorized" from MCP

The SANITY_API_READ_TOKEN is missing or invalid. Help the user generate a new token at sanity.io/manage → Project → API → Tokens with Viewer permissions.

"No documents found" / Empty results

Check the Agent Context Document's content filter (groqFilter):

Is the GROQ filter correct?
Are the document types spelled correctly?
Are there published documents matching the filter?

Tools not appearing

Check that mcpClient.tools() returns tools (log it)
Ensure the MCP URL is correct (project ID, dataset, slug)
Verify the agent context document is published

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