# OpenAPI Context Window Packer MCP for AI Agents MCP

> OpenAPI Context Window Packer helps you shrink massive OpenAPI and Swagger specifications so they actually fit inside your AI agent's context window without losing the critical parts of your API definition.

## Overview
- **Category:** developer-tools
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_XMZvUjc3wLVnJZVkg6dLaL15NgLbiiNTRgEG82fO/mcp
- **Tags:** openapi, swagger, compression, context-window, token-optimization

## Description

If you've ever tried to feed a massive Swagger file into an AI client only to have it crash or hallucinate because the token count exploded, you know the frustration. This MCP fixes that by letting you prune unnecessary paths and strip out unused components from your API definitions. You can target specific endpoints and keep only what matters, while tracing dependencies so you don't accidentally break the schema. Since this is part of the Vinkius catalog, you can connect it to any compatible client and immediately start optimizing your API context usage without managing complex local scripts.

## Tools

### compress_openapi_spec
Shrinks your OpenAPI file by removing paths and components not in your target list. This reduces the total token count significantly.

### analyze_endpoint_coverage
Checks if the specific endpoints you care about survived the pruning process. It ensures no critical routes were lost.

### trace_schema_dependencies
Follows the links between schemas to ensure all required data structures remain intact. This prevents broken tool-calling.

## Prompt Examples

**Prompt:** 
```
Can you shrink this API spec so it fits in 2000 tokens?
```

**Response:** 
```
I've processed the specification. Here is the summary:

| Metric | Value |
| :--- | :--- |
| **Original Size** | 15,400 tokens |
| **Compressed Size** | 1,850 tokens |
| **Reduction** | 88% |

The paths not in your target list have been removed.
```

**Prompt:** 
```
Check if my /payments endpoint is still in this pruned spec.
```

**Response:** 
```
I checked the current specification. The `/payments` endpoint is **present** and all associated schemas are intact.
```

**Prompt:** 
```
What schemas do I need to keep for the /login route?
```

**Response:** 
```
To ensure the `/login` route works correctly, the following schemas must be retained:

* `UserCredentials` 
* `AuthToken` 
* `LoginResponse` 
* `ErrorSchema`
```

## Capabilities

### Shrink massive API specs
Reduces token usage by removing irrelevant paths from your documentation.

### Verify endpoint presence
Checks that your critical routes survived the pruning process.

### Map schema requirements
Identifies every piece of data needed for a specific request to prevent breakage.

### Strip unused components
Cleans up your Swagger file by removing dead definitions and unused objects.

### Optimize context usage
Prepares lightweight API definitions that fit within strict token budgets.

## Use Cases

### Massive spec crashes agent
You have a 5MB Swagger file and your agent dies instantly. Use compress_openapi_spec to strip everything except the /orders and /users paths.

### Missing endpoint errors
After pruning, you aren't sure if the /auth route is still there. Run analyze_endpoint_coverage to confirm your critical routes are present.

### Broken schema definitions
You pruned a path but now the agent can't parse the response. Use trace_schema_dependencies to see exactly which schemas are required for that endpoint.

## Benefits

- Stop hitting token limits by using compress_openapi_spec to prune unnecessary API paths.
- Ensure your agent actually knows about your routes with analyze_endpoint_coverage.
- Prevent broken tool-calling by using trace_schema_dependencies to keep essential schemas.
- Reduce latency in your AI client by feeding it smaller, more efficient documentation.
- Automate the cleanup of bloated Swagger files without manual editing.

## How It Works

The bottom line is you get a functional, lightweight API definition that won't blow your token budget.

1. Connect your large OpenAPI or Swagger specification to the MCP.
2. Define which specific paths and endpoints you need to retain.
3. Receive a slimmed-down, token-efficient version of your spec ready for use.

## Frequently Asked Questions

**How can I prevent my OpenAPI spec from breaking Claude's context window?**
Use the OpenAPI Context Window Packer to prune unnecessary paths and components. This keeps your agent focused on relevant endpoints without hitting token limits.

**Will using OpenAPI Context Window Packer break my API definitions?**
No, because it uses dependency tracing to ensure all required schemas remain intact during the compression process.

**Can I use this MCP with Cursor or Windsurf?**
Yes, you can connect this MCP to any compatible client like Cursor, Windsurf, or VS Code to optimize your API documentation on the fly.

**How does OpenAPI Context Window Packer handle large Swagger files?**
It identifies unused components and paths, stripping them away until the specification fits within your target token budget.

**Can I verify if my critical endpoints are still present after pruning?**
Yes, you can use the coverage analysis tool to check that your essential routes were not accidentally removed during compression.

**How does the compression work?**
The engine uses deterministic pruning. It removes all paths not in your `targetEndpoints` and then traces `$ref` pointers to remove any schemas that are no longer reachable, ensuring the resulting spec is structurally valid.

**Will my API specification break after compression?**
No. The tool ensures schema integrity by performing a graph traversal. Any component or schema required by your target endpoints is strictly preserved.

**Can I control the size of the output?**
Yes. Using `compress_openapi_spec`, you can set a `maxTokenBudget`. The engine will iteratively truncate description fields until the specification fits within your specified budget.