• MAP
  • TEST
  • PROVE
  • HUMAN

How to audit context structure performance with your AI

Connect your agent to the Context Engineering Prover to run controlled experiments on your prompt architecture. You'll see exactly how structural changes impact model output.

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Short answer

How do I audit context structure performance with my AI?

Run structural mapping to visualize your current setup, then use removal tests to see if specific delimiters or orderings actually improve accuracy. You'll get a clear breakdown of how your context changes affect model performance.

Where every result lands.

The outcomes of your audit

The AI processes your context experiments and organizes the findings into these categories.

MAP

Structural maps

The AI uses structural mapping to show how your data is currently laid out. You get a visual representation of your context hierarchy.

TEST

Removal test results

The AI runs removal tests to see if deleting certain parts of the context changes the output. You'll know which parts are dead weight.

PROVE

Accuracy validation

The AI compares performance across different versions of your context. You get proof that a new delimiter actually helps.

HUMAN

Manual refinements

The AI flags context areas where the structural changes didn't yield clear results. You decide how to tweak the final prompt.

The workflow

What your AI does when the audit starts.

The AI handles the heavy lifting of running multiple iterations of your context structure.

  1. Map current structure

    The AI analyzes your existing prompt or data layout to create a baseline map of how information is organized.

    validate_context_engineering

  2. Run removal tests

    The AI systematically removes specific delimiters or sections to measure the impact on the model's response.

    validate_context_engineering

  3. Compare ordering

    The AI reorders your context blocks to see if moving information changes the model's reasoning or accuracy.

    validate_context_engineering

  4. Verify improvements

    The AI runs a final validation to confirm if the new structural changes meet your accuracy requirements.

    validate_context_engineering

Try it

Copy these to start.

Use these prompts to kick off your first audit.

Starting points

Swap the bracketed text for your specific prompt files or context blocks.

Context Engineering Prover Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_I58Q8JLd1uvhq1AqdUUqb9cpqMx6m1wNFStTwnjf/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — Context Engineering Prover capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "context-engineering-prover-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_I58Q8JLd1uvhq1AqdUUqb9cpqMx6m1wNFStTwnjf/mcp"
    }
  }
}
Copy into chat04

  • Run a structural mapping on my current prompt file in /prompts/system_v1.txt.

  • Perform a removal test on the delimiters used in my context block from the user_data.json file.

  • Compare the accuracy of my current context structure against a version where I move the instructions to the end.

  • Validate if adding XML tags to my context in the product_specs.md file improves model adherence.

  • Claude
  • ChatGPT
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  • Windsurf
  • Claude Code
  • JetBrains
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Start here

Connect Context Engineering Prover once, then ask.

Just link the MCP to your client. Your credentials stay encrypted, and you can start running audits immediately.

Connect Context Engineering Prover to your AI

FAQ

How the audit works

  • 01

    Can the AI rewrite my prompts automatically?

    No. The AI runs the tests and provides the data, but you are responsible for writing and applying the final prompt changes.

  • 02

    Does this tool change my original files?

    No. The AI only reads your files to perform the mapping and tests. It does not write or overwrite your source files.

  • 03

    How does it measure accuracy?

    It compares the model's responses across different structural iterations to see if specific changes lead to more consistent or correct outputs.

  • 04

    Can I audit context in a live database?

    The AI can only audit the context you provide through the files or text blocks you point it toward.

  • 05

    What happens if a removal test fails?

    If removing a piece of context doesn't change the output, the AI flags that the piece of information might be redundant or ignored.

  • More questions about Context Engineering Prover? The Connector page answers them. See everything the Context Engineering Prover Connector can do

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