# {{ package_name }}

{{ display_name }} MCP (Model Context Protocol) server, generated by [mcpify](https://github.com/guercheLE/mcpify).

Exposes exactly 3 tools — `search`, `get`, `call` — backed by an embedded semantic database (`mcp_store.db`), so an LLM never needs the full API surface in context.

## Install

```bash
uv sync
```

## Setup

```bash
uv run {{ project_name }} setup
```

Interactively collects the API URL and the credentials your chosen auth method needs, then lets you persist them as a `.env` file, a `config.json` file, or a ready-to-run CLI invocation.

## Usage

### Terminal Client (default)

```bash
uv run {{ project_name }} search "create an issue"
uv run {{ project_name }} get <operationId>
uv run {{ project_name }} call <operationId> --some-arg value
```

### Harness Server

```bash
uv run {{ project_name }} start                              # stdio transport (default)
uv run {{ project_name }} http --host 127.0.0.1 --port 3000  # HTTP transport
```

## Testing

```bash
uv run pytest
```

## Coverage

```bash
bash scripts/coverage.sh   # writes htmlcov/index.html + coverage.json
```

## Profiling

```bash
uv sync --extra profiling   # optional — installs memray for memory profiling
bash scripts/profile.sh     # writes profile/bottleneck-report.md
```

`profile/bottleneck-report.md` combines the coverage summary with CPU (cProfile) and, if `memray` is installed, memory hot-path summaries in one small text file — paste it into an LLM (or hand it to another tool) to find and fix bottlenecks. memray doesn't support Windows.

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Generated by mcpify — do not hand-edit generated files; re-run mcpify against an updated OpenAPI spec instead.
