# Building a free CLI agent with Pi, Ollama, Gemma 4, and Parallel

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pi_coder/
├── .mcp.json          # Parallel Search MCP endpoint
└── .pi/
    └── settings.json  # pointer to the Ollama provider

~/.pi/agent/
└── models.json        # defines the Ollama provider itself``` 
pi_coder/
├── .mcp.json          # Parallel Search MCP endpoint
└── .pi/
    └── settings.json  # pointer to the Ollama provider
 
~/.pi/agent/
└── models.json        # defines the Ollama provider itself
``` 

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pi install npm:pi-mcp-adapter``` 
pi install npm:pi-mcp-adapter
``` 

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{
  "mcpServers": {
    "parallel-search": {
      "url": "https://search.parallel.ai/mcp",
      "directTools": ["web_search", "web_fetch"]
    }
  }
}``` 
{
  "mcpServers": {
    "parallel-search": {
      "url": "https://search.parallel.ai/mcp",
      "directTools": ["web_search", "web_fetch"]
    }
  }
}
``` 

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{
  "providers": {
    "ollama": {
      "baseUrl": "http://localhost:11434/v1",
      "api": "openai-completions",
      "apiKey": "ollama",
      "models": [{ "id": "gemma4:26b" }]
    }
  }
}``` 
{
  "providers": {
    "ollama": {
      "baseUrl": "http://localhost:11434/v1",
      "api": "openai-completions",
      "apiKey": "ollama",
      "models": [{ "id": "gemma4:26b" }]
    }
  }
}
``` 

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{
  "defaultProvider": "ollama",
  "defaultModel": "gemma4:26b"
}``` 
{
  "defaultProvider": "ollama",
  "defaultModel": "gemma4:26b"
}
``` 

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npm install -g @mariozechner/pi-coding-agent
pi install npm:pi-mcp-adapter
ollama pull gemma4:26b
ollama pull gemma4:e4b
ollama serve &
# write ~/.pi/agent/models.json (see above)
cd pi_coder # contains .mcp.json + .pi/settings.json
pi``` 
npm install -g @mariozechner/pi-coding-agent
pi install npm:pi-mcp-adapter
ollama pull gemma4:26b
ollama pull gemma4:e4b
ollama serve &
# write ~/.pi/agent/models.json (see above)
cd pi_coder # contains .mcp.json + .pi/settings.json
pi
``` 

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# `brief` — a terminal news briefing CLI

A tiny CLI that turns a topic into a morning-coffee briefing in your terminal. You type a topic, it fetches what happened recently, and prints a short summary with sources.

## Usage

```bash
brief "ai agents"
brief "openai" --since 24h
brief "rust web frameworks" --bullets 5
```

Output (stdout, plain text with minimal ANSI):

```
🗞  ai agents — last 24h

• Anthropic shipped Opus 4.7 with 1M-token context…  [1]
• LangChain released v1.0 with a rewritten runtime…  [2]
• Researchers at CMU published a paper on…           [3]

Sources
[1] https://…
[2] https://…
[3] https://…
```

## Flags

| Flag | Default | Purpose |
| --- | --- | --- |
| `--since <duration>` | `24h` | Recency window (`6h`, `24h`, `7d`) |
| `--bullets <n>` | `5` | Number of bullets |
| `--json` | off | Emit structured JSON instead of prose |

## How it works

Three steps, one file:

1. **Search** — use `@modelcontextprotocol/sdk` with `StreamableHTTPClientTransport` to connect to `https://search.parallel.ai/mcp`, then `client.callTool({ name: "web_search", arguments: { ... } })`. Do **not** hand-roll JSON-RPC over `fetch` — the Streamable HTTP transport has an initialize handshake, session headers, and SSE framing that the SDK handles. When developing, run await client.listTools() once and log the web_search input schema before the first callTool — don't guess argument names; also log the results so you know the shape of the response. Pass `--since` in the search objective.
2. **Summarize** — hand the results to an LLM with a tight prompt: "Return N bullets. One sentence each. Cite each bullet with `[n]` matching the source index. No fluff." The LLM only produces text — it does not call tools, so any Ollama chat model works regardless of its tool-calling maturity.
3. **Print** — render bullets + a numbered source list. In `--json` mode, skip rendering and dump the structured object, e.g.:

```json
{
  "topic": "ai agents",
  "since": "24h",
  "bullets": [
    { "text": "Anthropic shipped Opus 4.7…", "source": 1 }
  ],
  "sources": [
    { "n": 1, "url": "https://…", "title": "…" }
  ]
}
```

## Stack

- **Language:** TypeScript (Node), single file, `npx`-runnable.
- **Search:** Parallel Search MCP. Docs: `https://docs.parallel.ai/integrations/mcp/search-mcp`. No API key required.
- **LLM:** Use ollama + gemma4:e4b (already installed).
- **Config:** No config required.
- **Install**: installable as a brief command on PATH (e.g. via npm link)

## Non-goals

- No caching, no database, no daemon. Run it, read it, close it.
- No scheduling or delivery (cron + `brief ... | mail` is the user's job).
- No multi-topic dashboards. One topic per invocation keeps the code tiny.

## Tips
- **Parallel Search MCP** Use Parallel Search MCP yourself to find the latest documentation for any packages. Use it to look up anything you have a question on.
- **No tool-calling from the LLM** The CLI code orchestrates: it calls the MCP, then passes results as plain text to the LLM for summarization. The LLM never calls tools, so Ollama's tool-call parsing quirks are irrelevant here.
- **Test** You have all that you need to do a full end-to-end test on your own. Do this before marking as complete. After fixing any bugs, test again until you have determined `brief` is working well.``` 
# `brief` — a terminal news briefing CLI
 
A tiny CLI that turns a topic into a morning-coffee briefing in your terminal. You type a topic, it fetches what happened recently, and prints a short summary with sources.
 
## Usage
 
```bash
brief "ai agents"
brief "openai" --since 24h
brief "rust web frameworks" --bullets 5
```
 
Output (stdout, plain text with minimal ANSI):
 
```
🗞  ai agents — last 24h
 
• Anthropic shipped Opus 4.7 with 1M-token context…  [1]
• LangChain released v1.0 with a rewritten runtime…  [2]
• Researchers at CMU published a paper on…           [3]
 
Sources
[1] https://…
[2] https://…
[3] https://…
```
 
## Flags
 
| Flag | Default | Purpose |
| --- | --- | --- |
| `--since <duration>` | `24h` | Recency window (`6h`, `24h`, `7d`) |
| `--bullets <n>` | `5` | Number of bullets |
| `--json` | off | Emit structured JSON instead of prose |
 
## How it works
 
Three steps, one file:
 
1. **Search** — use `@modelcontextprotocol/sdk` with `StreamableHTTPClientTransport` to connect to `https://search.parallel.ai/mcp`, then `client.callTool({ name: "web_search", arguments: { ... } })`. Do **not** hand-roll JSON-RPC over `fetch` — the Streamable HTTP transport has an initialize handshake, session headers, and SSE framing that the SDK handles. When developing, run await client.listTools() once and log the web_search input schema before the first callTool — don't guess argument names; also log the results so you know the shape of the response. Pass `--since` in the search objective.
2. **Summarize** — hand the results to an LLM with a tight prompt: "Return N bullets. One sentence each. Cite each bullet with `[n]` matching the source index. No fluff." The LLM only produces text — it does not call tools, so any Ollama chat model works regardless of its tool-calling maturity.
3. **Print** — render bullets + a numbered source list. In `--json` mode, skip rendering and dump the structured object, e.g.:
 
```json
{
  "topic": "ai agents",
  "since": "24h",
  "bullets": [
    { "text": "Anthropic shipped Opus 4.7…", "source": 1 }
  ],
  "sources": [
    { "n": 1, "url": "https://…", "title": "…" }
  ]
}
```
 
## Stack
 
- **Language:** TypeScript (Node), single file, `npx`-runnable.
- **Search:** Parallel Search MCP. Docs: `https://docs.parallel.ai/integrations/mcp/search-mcp`. No API key required.
- **LLM:** Use ollama + gemma4:e4b (already installed).
- **Config:** No config required.
- **Install**: installable as a brief command on PATH (e.g. via npm link)
 
## Non-goals
 
- No caching, no database, no daemon. Run it, read it, close it.
- No scheduling or delivery (cron + `brief ... | mail` is the user's job).
- No multi-topic dashboards. One topic per invocation keeps the code tiny.
 
## Tips
- **Parallel Search MCP** Use Parallel Search MCP yourself to find the latest documentation for any packages. Use it to look up anything you have a question on.
- **No tool-calling from the LLM** The CLI code orchestrates: it calls the MCP, then passes results as plain text to the LLM for summarization. The LLM never calls tools, so Ollama's tool-call parsing quirks are irrelevant here.
- **Test** You have all that you need to do a full end-to-end test on your own. Do this before marking as complete. After fixing any bugs, test again until you have determined `brief` is working well.
```