Prompt MCP Server

Local MCP server that optimizes rough prompt drafts using a local Ollama model. The prompt mcp server wraps that behind the Model Context Protocol, so an assistant can use it through 6 defined tools rather than through you.

What it actually does

Local MCP server that uses a local Ollama model as a "Prompt Engineer" to rewrite rough prompt drafts into structured, optimized prompts before you send them to a paid API (Claude, GPT-4o, etc.) — saving tokens and improving output quality on the paid model.

Adding it to your client

npm on npm is all you need. Most clients run it directly, so configuration is a few lines and a restart.

Its toolset

Everything the assistant can do here goes through one of these:

• Cursor — Navigate to Cursor Settings -> Features -> MCP -> Add new MCP server. - Name: prompt-enhancer - Type: command - Command: npx -y
• optimize_prompt — Optimizes a rough prompt draft using a local LLM before sending it to a paid API
• lint_prompt — Checks any prompt for common issues without making LLM calls. Detects unresolved placeholders ({{placeholder}}), suspect acronym expansions (against
• score_prompt — Judge-grades a prompt 1-5 on five dimensions: clarity, specificity, structure, guardrails, and token efficiency. Pass baseline to switch to
• generate_system_prompt — Drafts a system prompt for a given agent role, then auto-lints and auto-scores it before returning. Pass rigor: "both" to generate a terse and a
• check_health — Checks whether the configured LLM engine is reachable and ready to use — including whether the connected MCP client supports the sampling engine

Configuration

You will need 3 environment variables: OLLAMA_BASE_URL, OLLAMA_EXTRA_HEADERS, ANTHROPIC_API_KEY. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.

• Node.js 20+ - Option A (Local Ollama): Ollama running locally with a model pulled, e.g.: - Option B (Remote Ollama): Ollama running on a machine you control (e.g. a home server exposed through a tunnel), with OLLAMA_BASE_URL (and optionally OLLAMA_EXTRA_HEADERS for an authenticating proxy) pointed at it. See Remote Ollama endpoint

Caveats

• It runs with your machine's permissions. That is convenient and also the reason to think about what you point it at before you approve a tool call.
• Missing credentials fail quietly in some clients — if no tools show up, check the environment block first.
• MCP clients confirm each tool call by default. Leave that on until you have watched what the prompt mcp server does with a few real requests.

When to reach for it

This sits in the AI and media services group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Prompt's toolset — Cursor, optimize_prompt, lint_prompt and 3 more — is a fair guide to whether it matches your workflow. It is maintained by GitHub Actions; worth a glance at recent repository activity before you build anything load-bearing on it.

We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.