How to Use the Cohere (AI Platform) MCP in Cline

Q: Can Cline use Cohere (AI Platform) to write and run tests?

Yes. Cline uses generatetext to write the test code, runs your local test runner, and refactors any failures it finds.

Q: How does Cline know which Cohere (AI Platform) models are available?

Cline calls the listmodels tool to inspect available API endpoints. It then chooses the correct model for text generation or embedding tasks.

Q: How does this server handle large text inputs during code generation?

Cline uses tokenizetext to split large code blocks into precise segments. This ensures your payloads stay within the model limits.

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Connect Cohere (AI Platform) MCP to Cline

Create your Vinkius account to connect Cohere (AI Platform) to Cline and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Cohere (AI Platform) with Cline

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Generate embeddings inside Cline

The `generate_embeddings` tool identifies precise dense vector shapes to map semantic limits across your codebase. Cline runs this tool to vectorize your local files, saving the output directly into your vector database. If Cline detects a mismatch in your vector database schema, it writes a migration script, runs your test suite, and applies the changes. This MCP Server gives your agent the tools to manage semantic search pipelines without manual intervention.

Run Cohere document reranking inside Cline

The `rerank_documents` tool discovers routing arrays to structure specific contextual chunks for your queries. Cline uses this to find the exact code snippet or documentation page needed to solve a bug. Instead of guessing which file to edit, Cline passes your codebase context to the model and reads the ranked results. It then opens the top-ranked file and starts making edits immediately.

Classify workspace inputs directly with Cline

The `classify_inputs` tool evaluates static limits to categorize strings based on your custom classes inside this MCP Server. Cline uses this to tag open issues, sort user feedback files, or organize log outputs in your workspace. To ensure the inputs fit within API limits, Cline runs `tokenize_text` to measure the NLP context first. It then runs the classification and writes the results back to your project files.

Setup guide

Set up Cohere (AI Platform) MCP in Cline

Prerequisites

VS Code with Cline extension installed
Active Vinkius subscription with a valid endpoint token

1

Open Cline MCP settings

Click the Cline icon in the VS Code sidebar to open the Cline panel. Then click the MCP Servers icon (server stack) at the top-right corner of the panel.
2

Add a remote server

Click "Remote Servers" at the top, then click "Add Remote MCP". In the Name field, type cohere-ai-platform-mcp. In the URL field, paste your Vinkius endpoint: https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp. Get your token from cloud.vinkius.com.
3

Enable the server

After saving, the server appears in the Cline MCP panel. Toggle the switch to enable it. The status indicator turns green when the connection is live.
4

Start using tools

Return to the Cline chat and ask: "Check my latest Cohere (AI Platform) refund status." Cline will discover the available tools and request your approval before invoking each one — giving you full control over every action.

Cline MCP Settings

{
  "mcpServers": {
    "cohere-ai-platform-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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Common questions about Cohere (AI Platform) MCP in Cline

Open the Cline sidebar and click the MCP Servers icon. Use the interface to add the server URL, which connects Cline to the MCP tools immediately.

Yes. Cline uses `generate_text` to write the test code, runs your local test runner, and refactors any failures it finds.

Cline calls the `list_models` tool to inspect available API endpoints. It then chooses the correct model for text generation or embedding tasks.

Cline uses `tokenize_text` to split large code blocks into precise segments. This ensures your payloads stay within the model limits.

Yes. Your code strings and tokens are transmitted over encrypted TLS directly to Cohere's secure endpoints. The MCP Server executes inside an ephemeral V8 sandbox that destroys all session data as soon as the execution ends.

Use it with your favorite AI tools

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