Dial Your Context

Dial Your Context

Help a user create the Instructions field content for the Sanity Context MCP server. The goal is a concise set of pure deltas — only information the agent can't figure out from the auto-generated schema.

What you're building

The Sanity Context MCP server already provides the agent with:

• A compressed schema of all document types and fields
• A GROQ query tutorial (~194 lines)
• Response style guidance
• Tool descriptions for GROQ queries, semantic search, etc.

The Instructions field you're crafting gets injected as a ## Custom instructions section between ## Response style and ## Tools in the MCP's instructions blob. It should contain only what the schema doesn't make obvious:

• Counter-intuitive field names (e.g., body is actually a slug, hero is a reference to mediaAsset)
• Second-order reference chains the schema doesn't connect (e.g., "to find products with Dolby Atmos, chain product → productFeature and match on the feature's id field — the schema shows each hop but not the full path")
• Data quality issues the schema can't reveal (e.g., "the product type has a features array but it's always empty — use support-product instead")
• Required filters the agent must always apply (locale, draft status, etc.)
• Known data gaps confirmed by the user (e.g., "the subtitle field is unused — ignore it")
• Query patterns for common use cases that aren't obvious from the schema
• Fallback strategies when primary approaches fail

Never duplicate what the schema already communicates clearly.

Prerequisites

You need one of these to run this session:

Path A — Write access (recommended): A Sanity write token or the general Sanity MCP (OAuth). This lets you create a draft context doc, write instructions + filter to it during the session, and promote it to production when done. Production is never touched until you're ready.

Path B — URL params only: Use ?instructions= and ?groqFilter= URL query params on the MCP endpoint to test everything. At the end, provide the final content for the user to enter manually in Sanity Studio. Works with both base and document URLs.

Both paths are safe — neither modifies the production agent during the session.

Critical rules

1. Pure deltas only. If the schema makes it obvious, don't put it in Instructions.
2. Never generalize from small samples. Querying 3 docs and concluding "field X is always null" is the #1 failure mode. Every claim must be verified with the user before inclusion.
3. The user knows their data. Schema dialogue beats data exploration. Present the schema, ask questions, listen.
4. Verify every claim with evidence. For each line in the draft Instructions, show the query + result that supports it. The user confirms or corrects.
5. Keep it concise and factual. The compaction step (summarizing findings into Instructions) is where information gets lost or distorted. No creative interpretation. Short declarative sentences.

Workflow

Step 1: Connect & Clean Slate

Goal: Establish MCP access, set up a safe working environment.

Connect to the user's Sanity Context MCP server. Get the project ID and dataset from the user if not already known. The slug is only needed if they have an existing Sanity Context document.

Set up your working environment:

Path A (write access): Create a new draft context doc by copying the existing one (if any) to a new slug like tuning-draft. All exploration and iteration happens against this draft — the production agent is untouched.

Path B (no write access): Use URL query params throughout the session:

• ?instructions="" — forces a blank slate (ignores existing instructions)
• ?groqFilter=<expression> — applies a filter without writing to the context doc

Check if the context document already has instructions content:

• If yes, present the existing instructions to the user verbatim
• Ask: "Do you want to keep any of this, or start fresh?"
• Let the user decide — don't assume existing instructions are wrong
• If they have existing instructions from a previous session, you'll verify and refine each finding rather than starting from scratch

Verify you can query the dataset by running a simple GROQ query like *[0..2]._type to confirm access.

Output: Confirmed MCP access, safe working environment established (draft doc or URL params), any existing instructions surfaced to user.

Step 2: Schema Dialogue

Goal: Understand the dataset through conversation, not just exploration.

Retrieve the schema (the MCP provides this). Present the document types to the user in a clear list:

Here are the document types in your dataset: - article (14 fields) - author (8 fields) - category (5 fields) - ... Which of these are the ones your agent will need to work with?

This is a conversation, not a monologue. Ask the user:

1. Which types matter? "Which of these will your agent need to query? Any types here that are internal/system types the agent should ignore?"
2. What's misleading? "Any field names that don't mean what they sound like? Fields that are unused or deprecated?"
3. What are the relationships? "How do these types connect? For example, do articles reference authors? How — direct reference, array of references, something else?"
4. Any required filters? "Does the agent need to always filter by locale, published status, or any other field?"
5. What's the primary content language? If i18n is involved, clarify the pattern.

Suggest a filter. The MCP supports a groqFilter — a full GROQ expression that scopes which documents the agent can access. This is high-leverage — it reduces noise significantly and prevents the agent from querying irrelevant types.

The filter is a GROQ expression string, not just a type list. This means you can carve out exactly the document set you want:

• Simple type filter: _type in ["product", "support-article", "productFeature"]
• Exclude drafts: !(_id in path("drafts.**")) && _type in ["product", "article"]
• Locale filter: _type in ["product", "article"] && lang == "en-us"
• Complex: _type in ["product", "article"] && !(_id in path("drafts.**")) && defined(title)

Based on the conversation, propose a filter:

Based on what you've told me, I'd suggest this filter: `` _type in ["article", "author", "category", "tag"] ` This means the agent won't see siteSettings, redirect, migration`, etc. Does that sound right?

Apply the filter immediately. Once the user agrees:

• Path A (write access): Write the groqFilter field to the draft context doc
• Path B (URL params): Add ?groqFilter=<expression> to all subsequent MCP calls

All exploration from this point forward should use the agreed filter.

Output: A shared understanding of which types matter, known quirks, relationships, and an active filter. There's no point exploring types the production agent won't see.

Step 3: Expected Questions

Goal: Get concrete examples of what the production agent will be asked.

Ask the user:

What questions will people ask the agent that uses this context? Give me 5-20 examples — the more realistic, the better.

Examples might be:

• "Which speakers support Dolby Atmos?"
• "How do I fix WiFi connection issues?"
• "What's the return policy for refurbished products?"
• "Compare the features of product X and product Y"

These questions drive the exploration in Step 4. They tell you what query patterns actually matter.

For simple datasets, 5 questions is fine. For complex ones, push for 15-20.

Output: A numbered list of expected questions.

Step 4: Explore & Verify

Goal: Answer each expected question using the MCP, track what works and what doesn't.