> For AI agents: the complete Sanity documentation index is available at [https://www.sanity.io/docs/llms.txt](https://www.sanity.io/docs/llms.txt).

# Inline initial context into your system prompt

Fetch initial context over HTTP and drop the tool from your agent to remove a round trip from every conversation.

Agents connected to a Context MCP endpoint call the `initial_context` tool first to orient themselves, which costs a round trip at the start of every conversation. If you control the system prompt (you're building a custom agent rather than plugging into a third-party client), you can fetch the same payload over HTTP, inline it, and drop the tool. This guide shows how.

## Prerequisites

- A configured Context MCP endpoint. See [Configure a Context MCP endpoint](https://www.sanity.io/docs/context/configure-mcp).
- Your organization ID and the endpoint's URL name, both of which appear in the endpoint's URL.
- An organization API token with Context Viewer permissions, created under Manage > API > Tokens at the organization level. Viewer is the least privilege that works; Editor also works.
- A JavaScript or TypeScript agent whose system prompt you control, with `ai@^7`, `@ai-sdk/mcp@^2`, and `@ai-sdk/anthropic@^4` installed.

## What initial context contains

Initial context depends on the endpoint's sources. For a dataset source it returns a compressed schema overview along with instructions on how to query your content. For Knowledge Base sources it returns the outline of the Knowledge Bases the endpoint serves. An endpoint with both gets both in the same payload. Each part also carries query-efficiency and grounding instructions, so the text you inline contains behavioral instructions as well as data. Reconcile them with your own.

## Fetch initial context over HTTP

Append `/initial-context` to the MCP endpoint URL path, before any query parameters, using the same auth header:

**Terminal**

```sh
curl https://api.sanity.io/v2026-11-03/context/organizations/YOUR_ORGANIZATION_ID/mcp/YOUR_ENDPOINT_NAME/initial-context \
  -H "Authorization: Bearer $SANITY_ORGANIZATION_TOKEN"
```

`YOUR_ORGANIZATION_ID` is your organization's ID. `YOUR_ENDPOINT_NAME` is the endpoint's URL name: the kebab-case name you gave it when you created it, which cannot be changed afterwards.

`SANITY_ORGANIZATION_TOKEN` holds the organization API token from the prerequisites. A project dataset-read token does not carry the organization-level permission this route requires, and the request fails with a 403. Keep the token server-side: it carries organization-level permissions, so never ship it in browser-visible code.

The response is `text/plain`. Its markdown headings are shifted down one level by default, so a top-level `#` arrives as `##` and nests under headings of your own. Pass `?heading_offset=0` to keep them as authored, or a higher number to nest them deeper.

## Inline the payload and drop the tool

Install the Vercel AI SDK packages first. All three share an internal provider dependency, so their majors have to match. Pairing `ai@6` with a newer `@ai-sdk/mcp` compiles to type errors on `model` and `tools` that name neither package, and look like a mistake in your own code.

**npm**

```shell
npm install ai@^7 @ai-sdk/mcp@^2 @ai-sdk/anthropic@^4
```

**pnpm**

```shell
pnpm add ai@^7 @ai-sdk/mcp@^2 @ai-sdk/anthropic@^4
```

**yarn**

```shell
yarn add ai@^7 @ai-sdk/mcp@^2 @ai-sdk/anthropic@^4
```

**bun**

```shell
bun add ai@^7 @ai-sdk/mcp@^2 @ai-sdk/anthropic@^4
```

Fetch the payload at startup, concatenate it with your own instructions and pass the result as `instructions`, then remove `initial_context` from the tool set you hand over. With the Vercel AI SDK, the MCP client comes from `@ai-sdk/mcp`, which is a separate package from `ai`:

**index.ts**

```typescript
import {createMCPClient} from '@ai-sdk/mcp'
import {anthropic} from '@ai-sdk/anthropic'
import {generateText, stepCountIs} from 'ai'

const endpoint =
  'https://api.sanity.io/v2026-11-03/context/organizations/YOUR_ORGANIZATION_ID/mcp/YOUR_ENDPOINT_NAME'
const headers = {Authorization: `Bearer ${process.env.SANITY_ORGANIZATION_TOKEN}`}

// The same payload the curl command returned, fetched once at startup
const res = await fetch(`${endpoint}/initial-context`, {headers})
// Returns null on failure so the agent can fall back to the initial_context tool
const initialContext = res.ok ? await res.text() : null
if (!res.ok) console.error(`Initial context request failed: HTTP ${res.status} ${await res.text()}`)

const mcpClient = await createMCPClient({
  transport: {type: 'http', url: endpoint, headers},
})

const mcpTools = await mcpClient.tools()
// Drop initial_context only when its payload is inlined; otherwise the agent needs the tool
if (initialContext) delete mcpTools.initial_context

const yourInstructions = 'You are a support agent for Acme safety equipment.'

const result = await generateText({
  model: anthropic('claude-sonnet-5-5'),
  instructions: initialContext ? `${yourInstructions}\n\n${initialContext}` : yourInstructions,
  tools: mcpTools,
  stopWhen: stepCountIs(20),
  prompt: 'Which glove grades are rated for solvent handling?',
})

console.log(result.text)
```

## Keep the inlined payload fresh

An inlined payload is a snapshot. It goes stale when the schema behind a dataset source changes, when a build rewrites a Knowledge Base outline, or when the endpoint's instructions change. Refetch it on deploy at minimum, and after any Knowledge Base rebuild.

## Next steps

- [Context MCP tools](https://www.sanity.io/docs/context/mcp-tools). The rest of the tools an endpoint serves.
- [Sanity Context patterns and best practices](https://www.sanity.io/docs/context/best-practices). Shaping agent behavior once the basics work.