docs(coding-cli-setup): split per-CLI docs into their own files
Move docs/coding-cli-setup.md to docs/coding-cli-setup/ with one file per CLI (claude-code, kimi-cli, opencode, qwen-code) plus a shared index.md for the gateway intro, tool-calling risk note, and summary table. Also fixes the qwen-code doc: context sizes are per-slot (LLAMA_CTX_SIZE / LLAMA_PARALLEL), not raw LLAMA_CTX_SIZE (same fix applied to OpenCode's limit.context); documents the fastModel classifier provider and its own context math; adds the omniroute-search MCP server (SearXNG web search) and Auto Mode permissions tuning that were missing from the original qwen-code section. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
This commit is contained in:
@@ -14,7 +14,7 @@ See the wayfinder map ([issue #1](https://git.arthurerlich.de/haylan/LLM-Server/
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llama.cpp's own API is internal-only — everything routes through the AI gateway below.
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llama.cpp's own API is internal-only — everything routes through the AI gateway below.
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Pointing Claude Code CLI, Kimi CLI, or OpenCode CLI at the local endpoint: see [`docs/coding-cli-setup.md`](docs/coding-cli-setup.md).
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Pointing Claude Code CLI, Kimi CLI, OpenCode CLI, or Qwen Code CLI at the local endpoint: see [`docs/coding-cli-setup/`](docs/coding-cli-setup/index.md).
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**Known risk**: Qwen3.8-27B's tool-calling reliability against llama.cpp's Anthropic shim is not yet verified (open upstream parser bugs against its model lineage) — see `docs/research/qwen3.8-27b-tool-calling.md`.
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**Known risk**: Qwen3.8-27B's tool-calling reliability against llama.cpp's Anthropic shim is not yet verified (open upstream parser bugs against its model lineage) — see `docs/research/qwen3.8-27b-tool-calling.md`.
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@@ -26,15 +26,10 @@ An [AI gateway/proxy](https://git.arthurerlich.de/haylan/LLM-Server/issues/9) fr
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- Dashboard (key/provider management): LAN/host-only, never published to the internet — see `docs/network-access.md`.
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- Dashboard (key/provider management): LAN/host-only, never published to the internet — see `docs/network-access.md`.
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- Issuing a key for a new workload: [`docs/proxy-key-onboarding.md`](docs/proxy-key-onboarding.md).
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- Issuing a key for a new workload: [`docs/proxy-key-onboarding.md`](docs/proxy-key-onboarding.md).
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Coding CLIs (see [`docs/coding-cli-setup.md`](docs/coding-cli-setup.md)) route through the gateway — llama-server has no published host port. **Not yet verified**: none of this has been smoke-tested on real hardware yet — see [issue #31](https://git.arthurerlich.de/haylan/LLM-Server/issues/31)'s tickets for the open items (provider registration, per-workload key minting).
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Coding CLIs (see [`docs/coding-cli-setup/`](docs/coding-cli-setup/index.md)) route through the gateway — llama-server has no published host port. **Not yet verified**: none of this has been smoke-tested on real hardware yet — see [issue #31](https://git.arthurerlich.de/haylan/LLM-Server/issues/31)'s tickets for the open items (provider registration, per-workload key minting).
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**Note on this choice**: OmniRoute's own docs (`docs/security/STEALTH_GUIDE.md`, `MITM-TPROXY-DECRYPT.md`, `PUBLIC_CREDS.md` in its repo) describe shipped features for evading AI-provider client detection, system-wide HTTPS interception via a locally-installed root CA, and hiding credentials from secret scanners. None of that is used by this stack's configuration, but it's a real characteristic of the upstream project — see issue #31's Notes for the full research trail before extending this integration further.
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**Note on this choice**: OmniRoute's own docs (`docs/security/STEALTH_GUIDE.md`, `MITM-TPROXY-DECRYPT.md`, `PUBLIC_CREDS.md` in its repo) describe shipped features for evading AI-provider client detection, system-wide HTTPS interception via a locally-installed root CA, and hiding credentials from secret scanners. None of that is used by this stack's configuration, but it's a real characteristic of the upstream project — see issue #31's Notes for the full research trail before extending this integration further.
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### Web search
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### Web search
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The gateway also fronts SearXNG-backed web search — see `docs/research/litellm-searxng-search.md` for the original research (still applicable — same standalone-endpoint pattern, see issue #31's #35).
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The gateway also fronts SearXNG-backed web search — see `docs/research/litellm-searxng-search.md` for the original research (still applicable — same standalone-endpoint pattern, see issue #31's #35).
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## What's not here
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- **Open WebUI** — this stack has no chat UI; every client is a coding CLI. Removed rather than kept idle.
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- **Gateway-level knowledgebase/memory** (`litellm-pgvector`, `pgvector-db`, a dedicated embedding model) — removed as unwanted, unrelated to OmniRoute's own lack of parity with it (see issue #31's #34). Superseded by OmniRoute's own built-in memory feature (opt-in via the dashboard, Settings → Memory): vector store is its bundled `sqlite-vec`, embeddings are a local ONNX model (Transformers.js, ~400MB, downloaded into the `omniroute-data` volume on first use) — no external services, no static config here.
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@@ -1,91 +0,0 @@
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# Pointing a coding-agent CLI at this stack
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This stack routes through the [AI gateway](https://git.arthurerlich.de/haylan/LLM-Server/issues/9) (OmniRoute, see issue #31) rather than talking to llama.cpp directly — llama.cpp's own port is internal-only now (see `docker-compose.yml`). The gateway exposes:
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- **OpenAI-compatible**: `http://<ai-box>:${OMNIROUTE_PORT:-4000}/v1`
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- **Anthropic Messages API** (OmniRoute's own `/v1/messages` endpoint, translating to the OpenAI-compatible backend): `http://<ai-box>:${OMNIROUTE_PORT:-4000}`
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Both serve the same underlying model — `Qwen3.8-27B-UD-Q4_K_XL.gguf`, registered in the gateway (naming is yours to pick when adding the llama-cpp provider connection — this doc assumes `qwen3.8-27b-local` for continuity) — behind whichever wire format the client speaks.
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`<ai-box>` is this machine's LAN address, or `proxy-ai.home` if your local DNS resolves that hostname directly to the box — see `docs/network-access.md`. If you're running a coding CLI from this machine itself, `localhost` works too.
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**Each CLI needs its own virtual key** — create one per docs/proxy-key-onboarding.md (omniroute's dashboard, `<workload>-<purpose>` naming, e.g. `claude-code-cli`, `kimi-cli`, `opencode-cli`). No budget set by default. These are the machine's interactive/high-priority workloads per `docs/proxy-request-priority.md`.
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> **Read this before relying on it for real work.** Qwen3.8-27B's tool-calling has **documented, open llama.cpp upstream bugs** (parser fails on text before `<tool_call>`, tool calls emitted as inert XML inside thinking blocks — see `docs/research/qwen3.8-27b-tool-calling.md`). Every setup below inherits this risk identically, regardless of which CLI or wire format you use. Don't trust it for unattended multi-step agentic work until you've run the smoke test in [issue #5](https://git.arthurerlich.de/haylan/LLM-Server/issues/5) (and the proxy-specific smoke test in [issue #17](https://git.arthurerlich.de/haylan/LLM-Server/issues/17)).
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## Claude Code CLI
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Claude Code speaks the **Anthropic Messages API** — point it at the gateway's unified endpoint, not llama.cpp directly:
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```bash
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export ANTHROPIC_BASE_URL=http://<ai-box>:${OMNIROUTE_PORT:-4000}
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export ANTHROPIC_API_KEY=<claude-code-cli virtual key>
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claude
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```
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Requires llama.cpp's `--jinja` flag (already set in `docker-compose.yml`) — without it, tool-use requests fail outright.
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## Kimi CLI
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Kimi CLI speaks plain **OpenAI Chat Completions**. Configure a provider block in its config file (`config.toml`):
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```toml
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[providers.openai]
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type = "openai"
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base_url = "http://<ai-box>:${OMNIROUTE_PORT:-4000}/v1"
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api_key = "<kimi-cli virtual key>"
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```
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If Kimi CLI's response parsing gets confused by Qwen's `<think>...</think>` reasoning tags, check its `reasoning_key` setting — it's configurable for non-standard local server responses.
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||||||
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||||||
## OpenCode CLI
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||||||
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||||||
Confirmed project: **`anomalyco/opencode`** (renamed from `sst/opencode` — don't confuse with the unrelated `opencode-ai/opencode` Go TUI). Docs: https://opencode.ai/docs/
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||||||
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||||||
**Install**:
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||||||
```bash
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||||||
curl -fsSL https://opencode.ai/install | bash
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||||||
```
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||||||
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||||||
**Config** (`opencode.json`, project root or `~/.config/opencode/opencode.json`):
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||||||
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||||||
```json
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{
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"$schema": "https://opencode.ai/config.json",
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"provider": {
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"aiproxy": {
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"npm": "@ai-sdk/openai-compatible",
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"name": "AI proxy (local)",
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"options": {
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"baseURL": "http://<ai-box>:${OMNIROUTE_PORT:-4000}/v1",
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"apiKey": "<opencode-cli virtual key>"
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},
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"models": {
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"qwen3.8-27b-local": {
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"name": "Qwen3.8-27B",
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"limit": { "context": 65536, "output": 8192 }
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}
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}
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}
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}
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}
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```
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Set `limit.context` to match whatever `LLAMA_CTX_SIZE` this stack is actually running with (`.env`), not a value assumed from the model card — OpenCode uses it for its own context-management bookkeeping, not the server.
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Select the model with `aiproxy/qwen3.8-27b-local`.
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||||||
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||||||
**OpenCode-specific risks** (on top of the shared Qwen3.8-27B tool-calling risk above):
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||||||
- Requires llama.cpp's `--jinja` flag (already set) — without it, OpenCode's unconditional tool-call scaffolding gets a 500.
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||||||
- [anomalyco/opencode#20669](https://github.com/anomalyco/opencode/issues/20669) (closed as "not planned" — a live, unfixed risk): OpenCode's `bash` tool crashes if the model omits the optional `description` field on a tool call; some local backends return `finish_reason: tool_calls` with an empty array, which can hang the agent loop instead of stopping cleanly.
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||||||
- Thinking-mode handling (`options.reasoningEffort`) is undocumented for models that emit inline `<think>` tags rather than a native reasoning API field — expect no effect from that config on this model; untested.
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||||||
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||||||
## Summary
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||||||
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||||||
| CLI | Wire format | Endpoint | Config |
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||||||
|---|---|---|---|
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||||||
| Claude Code | Anthropic Messages | `http://<ai-box>:${OMNIROUTE_PORT:-4000}` | `ANTHROPIC_BASE_URL` env var |
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||||||
| Kimi CLI | OpenAI Chat Completions | `http://<ai-box>:${OMNIROUTE_PORT:-4000}/v1` | `config.toml` provider block |
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||||||
| OpenCode | OpenAI Chat Completions | `http://<ai-box>:${OMNIROUTE_PORT:-4000}/v1` | `opencode.json` provider block |
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||||||
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||||||
Further reading: `docs/research/qwen3.8-27b-tool-calling.md`, `docs/research/opencode-cli-setup.md`, `docs/proxy-key-onboarding.md`.
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@@ -0,0 +1,13 @@
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# Claude Code CLI
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||||||
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||||||
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[← back to overview](index.md)
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||||||
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||||||
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Claude Code speaks the **Anthropic Messages API** — point it at the gateway's unified endpoint, not llama.cpp directly:
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||||||
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||||||
|
```bash
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||||||
|
export ANTHROPIC_BASE_URL=http://<ai-box>:${OMNIROUTE_PORT:-4000}
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||||||
|
export ANTHROPIC_API_KEY=<claude-code-cli virtual key>
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||||||
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claude
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||||||
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```
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||||||
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||||||
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Requires llama.cpp's `--jinja` flag (already set in `docker-compose.yml`) — without it, tool-use requests fail outright.
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||||||
@@ -0,0 +1,34 @@
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|||||||
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# Pointing a coding-agent CLI at this stack
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||||||
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||||||
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[← back to README](../../README.md)
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||||||
|
|
||||||
|
This stack routes through the [AI gateway](https://git.arthurerlich.de/haylan/LLM-Server/issues/9) (OmniRoute, see issue #31) rather than talking to llama.cpp directly — llama.cpp's own port is internal-only now (see `docker-compose.yml`). The gateway exposes:
|
||||||
|
|
||||||
|
- **OpenAI-compatible**: `http://<ai-box>:${OMNIROUTE_PORT:-4000}/v1`
|
||||||
|
- **Anthropic Messages API** (OmniRoute's own `/v1/messages` endpoint, translating to the OpenAI-compatible backend): `http://<ai-box>:${OMNIROUTE_PORT:-4000}`
|
||||||
|
|
||||||
|
Both serve the same underlying model — `Qwen3.8-27B-UD-Q4_K_XL.gguf`, registered in the gateway (naming is yours to pick when adding the llama-cpp provider connection — these docs assume `qwen3.8-27b-local` for continuity) — behind whichever wire format the client speaks.
|
||||||
|
|
||||||
|
`<ai-box>` is this machine's LAN address, or `proxy-ai.home` if your local DNS resolves that hostname directly to the box — see `docs/network-access.md`. If you're running a coding CLI from this machine itself, `localhost` works too.
|
||||||
|
|
||||||
|
**Each CLI needs its own virtual key** — create one per docs/proxy-key-onboarding.md (omniroute's dashboard, `<workload>-<purpose>` naming, e.g. `claude-code-cli`, `kimi-cli`, `opencode-cli`). No budget set by default. These are the machine's interactive/high-priority workloads per `docs/proxy-request-priority.md`.
|
||||||
|
|
||||||
|
> **Read this before relying on it for real work.** Qwen3.8-27B's tool-calling has **documented, open llama.cpp upstream bugs** (parser fails on text before `<tool_call>`, tool calls emitted as inert XML inside thinking blocks — see `docs/research/qwen3.8-27b-tool-calling.md`). Every CLI below inherits this risk identically, regardless of wire format. Don't trust it for unattended multi-step agentic work until you've run the smoke test in [issue #5](https://git.arthurerlich.de/haylan/LLM-Server/issues/5) (and the proxy-specific smoke test in [issue #17](https://git.arthurerlich.de/haylan/LLM-Server/issues/17)).
|
||||||
|
|
||||||
|
## Per-CLI setup
|
||||||
|
|
||||||
|
- [Claude Code CLI](claude-code.md)
|
||||||
|
- [Kimi CLI](kimi-cli.md)
|
||||||
|
- [OpenCode CLI](opencode.md)
|
||||||
|
- [Qwen Code CLI](qwen-code.md)
|
||||||
|
|
||||||
|
## Summary
|
||||||
|
|
||||||
|
| CLI | Wire format | Endpoint | Config |
|
||||||
|
|---|---|---|---|
|
||||||
|
| [Claude Code](claude-code.md) | Anthropic Messages | `http://<ai-box>:${OMNIROUTE_PORT:-4000}` | `ANTHROPIC_BASE_URL` env var |
|
||||||
|
| [Kimi CLI](kimi-cli.md) | OpenAI Chat Completions | `http://<ai-box>:${OMNIROUTE_PORT:-4000}/v1` | `config.toml` provider block |
|
||||||
|
| [OpenCode](opencode.md) | OpenAI Chat Completions | `http://<ai-box>:${OMNIROUTE_PORT:-4000}/v1` | `opencode.json` provider block |
|
||||||
|
| [Qwen Code](qwen-code.md) | OpenAI Chat Completions (2 models: chat + `fastModel`) | `http://<ai-box>:${OMNIROUTE_PORT:-4000}/v1` | `~/.qwen/settings.json` `modelProviders.openai` |
|
||||||
|
|
||||||
|
Further reading: `docs/research/qwen3.8-27b-tool-calling.md`, `docs/proxy-key-onboarding.md`.
|
||||||
@@ -0,0 +1,14 @@
|
|||||||
|
# Kimi CLI
|
||||||
|
|
||||||
|
[← back to overview](index.md)
|
||||||
|
|
||||||
|
Kimi CLI speaks plain **OpenAI Chat Completions**. Configure a provider block in its config file (`config.toml`):
|
||||||
|
|
||||||
|
```toml
|
||||||
|
[providers.openai]
|
||||||
|
type = "openai"
|
||||||
|
base_url = "http://<ai-box>:${OMNIROUTE_PORT:-4000}/v1"
|
||||||
|
api_key = "<kimi-cli virtual key>"
|
||||||
|
```
|
||||||
|
|
||||||
|
If Kimi CLI's response parsing gets confused by Qwen's `<think>...</think>` reasoning tags, check its `reasoning_key` setting — it's configurable for non-standard local server responses.
|
||||||
@@ -0,0 +1,45 @@
|
|||||||
|
# OpenCode CLI
|
||||||
|
|
||||||
|
[← back to overview](index.md)
|
||||||
|
|
||||||
|
Confirmed project: **`anomalyco/opencode`** (renamed from `sst/opencode` — don't confuse with the unrelated `opencode-ai/opencode` Go TUI). Docs: https://opencode.ai/docs/
|
||||||
|
|
||||||
|
**Install**:
|
||||||
|
```bash
|
||||||
|
curl -fsSL https://opencode.ai/install | bash
|
||||||
|
```
|
||||||
|
|
||||||
|
**Config** (`opencode.json`, project root or `~/.config/opencode/opencode.json`):
|
||||||
|
|
||||||
|
```json
|
||||||
|
{
|
||||||
|
"$schema": "https://opencode.ai/config.json",
|
||||||
|
"provider": {
|
||||||
|
"aiproxy": {
|
||||||
|
"npm": "@ai-sdk/openai-compatible",
|
||||||
|
"name": "AI proxy (local)",
|
||||||
|
"options": {
|
||||||
|
"baseURL": "http://<ai-box>:${OMNIROUTE_PORT:-4000}/v1",
|
||||||
|
"apiKey": "<opencode-cli virtual key>"
|
||||||
|
},
|
||||||
|
"models": {
|
||||||
|
"qwen3.8-27b-local": {
|
||||||
|
"name": "Qwen3.8-27B",
|
||||||
|
"limit": { "context": 65536, "output": 8192 }
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
Set `limit.context` to the *per-slot* context this stack actually serves — `LLAMA_CTX_SIZE / LLAMA_PARALLEL` from `.env` (262144 / 2 = 131072 by default), not raw `LLAMA_CTX_SIZE` and not a value assumed from the model card: llama.cpp divides `--ctx-size` across concurrent slots, so each request only gets one slot's share. OpenCode uses this for its own context-management bookkeeping, not the server.
|
||||||
|
|
||||||
|
Select the model with `aiproxy/qwen3.8-27b-local`.
|
||||||
|
|
||||||
|
**OpenCode-specific risks** (on top of the shared Qwen3.8-27B tool-calling risk — see [overview](index.md)):
|
||||||
|
- Requires llama.cpp's `--jinja` flag (already set) — without it, OpenCode's unconditional tool-call scaffolding gets a 500.
|
||||||
|
- [anomalyco/opencode#20669](https://github.com/anomalyco/opencode/issues/20669) (closed as "not planned" — a live, unfixed risk): OpenCode's `bash` tool crashes if the model omits the optional `description` field on a tool call; some local backends return `finish_reason: tool_calls` with an empty array, which can hang the agent loop instead of stopping cleanly.
|
||||||
|
- Thinking-mode handling (`options.reasoningEffort`) is undocumented for models that emit inline `<think>` tags rather than a native reasoning API field — expect no effect from that config on this model; untested.
|
||||||
|
|
||||||
|
Further reading: `docs/research/opencode-cli-setup.md`.
|
||||||
@@ -0,0 +1,116 @@
|
|||||||
|
# Qwen Code CLI
|
||||||
|
|
||||||
|
[← back to overview](index.md)
|
||||||
|
|
||||||
|
Qwen Code speaks plain **OpenAI Chat Completions**, and — unlike the other CLIs — needs *two* models: the main chat model, and a `fastModel` for Auto Mode's action classifier (a separate, always-resident, always-fast instance so classification doesn't queue behind chat prefill; see `docker-compose.yml`'s `llama-server-fast` service and `docs/research/fast-model-choice.md`). Both are registered as separate providers in OmniRoute but reachable through the same gateway URL. Config lives in `~/.qwen/settings.json`:
|
||||||
|
|
||||||
|
```json
|
||||||
|
{
|
||||||
|
"modelProviders": {
|
||||||
|
"openai": [
|
||||||
|
{
|
||||||
|
"id": "<main-model-provider-id-in-omniroute>",
|
||||||
|
"name": "qwen3.8-27b-local",
|
||||||
|
"envKey": "OMNIROUTE_API_KEY",
|
||||||
|
"baseUrl": "http://<ai-box>:${OMNIROUTE_PORT:-4000}/v1",
|
||||||
|
"generationConfig": { "contextWindowSize": 131072 }
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "<fast-model-provider-id-in-omniroute>",
|
||||||
|
"name": "qwen3.8-27b-classifier",
|
||||||
|
"envKey": "OMNIROUTE_API_KEY",
|
||||||
|
"baseUrl": "http://<ai-box>:${OMNIROUTE_PORT:-4000}/v1",
|
||||||
|
"generationConfig": {
|
||||||
|
"contextWindowSize": 8192,
|
||||||
|
"extra_body": { "chat_template_kwargs": { "enable_thinking": false } }
|
||||||
|
}
|
||||||
|
}
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"security": { "auth": { "selectedType": "openai" } },
|
||||||
|
"model": {
|
||||||
|
"name": "<main-model-provider-id-in-omniroute>",
|
||||||
|
"baseUrl": "http://<ai-box>:${OMNIROUTE_PORT:-4000}/v1"
|
||||||
|
},
|
||||||
|
"fastModel": "<fast-model-provider-id-in-omniroute>"
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
- `envKey` names the environment variable Qwen Code reads the virtual key from — set `OMNIROUTE_API_KEY=<qwen-code-cli virtual key>` before launching. Both providers can share one virtual key (as above); split it into two if you want separate usage tracking for chat vs. classifier calls.
|
||||||
|
- **`contextWindowSize` is per-slot, not `LLAMA_CTX_SIZE` itself** — llama.cpp divides `--ctx-size` across `LLAMA_PARALLEL` concurrent slots, and each request only gets one slot's share (same correction applies to OpenCode's `limit.context`). Compute it per model from `.env`:
|
||||||
|
- Main model: `LLAMA_CTX_SIZE / LLAMA_PARALLEL` = `262144 / 2` = **131072**.
|
||||||
|
- Fast model: `LLAMA_FAST_CTX_SIZE / LLAMA_FAST_PARALLEL` = `8192 / 1` = **8192**. Undersizing this one specifically breaks Auto Mode ("Classifier stage 1 unavailable") once `hints.allow`/`softDeny`/`hardDeny` entries and recent-action history push a classifier call past it — see the `LLAMA_FAST_CTX_SIZE` comment in `.env.example` before raising it instead of `LLAMA_FAST_PARALLEL`.
|
||||||
|
- `enable_thinking: false` on the fast model matters: the fast model file (`Qwen3-4B-Instruct-2507`) is already non-thinking, but this also suppresses `<think>` output on any fast-model swap that isn't, keeping classifier responses parseable.
|
||||||
|
- Qwen Code also recognizes `advisorModel`, `visionModel`, `compactionModel`, `imageModel` for other model roles — none are wired up in this stack; only `fastModel` is required.
|
||||||
|
|
||||||
|
## Web search via OmniRoute
|
||||||
|
|
||||||
|
Qwen Code's own built-in web search (`tools.webSearch.enabled`) has nothing to search with here — leave it `false`. Instead this stack's SearXNG-backed search (README §"Web search") is exposed through a thin stdio MCP wrapper around OmniRoute's `/v1/search` REST endpoint (that endpoint isn't itself MCP — OmniRoute's real MCP surface is admin-only/LOCAL_ONLY-gated). Save this as e.g. `~/.qwen/mcp-servers/omniroute-search/index.mjs` (needs `@modelcontextprotocol/sdk` and `zod`: `npm init -y && npm i @modelcontextprotocol/sdk zod` in that directory):
|
||||||
|
|
||||||
|
```js
|
||||||
|
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
|
||||||
|
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
|
||||||
|
import { z } from "zod";
|
||||||
|
|
||||||
|
const BASE_URL = process.env.OMNIROUTE_BASE_URL || "http://proxy-ai.home";
|
||||||
|
const API_KEY = process.env.OMNIROUTE_API_KEY;
|
||||||
|
|
||||||
|
if (!API_KEY) {
|
||||||
|
console.error("OMNIROUTE_API_KEY is not set in the environment.");
|
||||||
|
process.exit(1);
|
||||||
|
}
|
||||||
|
|
||||||
|
const server = new McpServer({ name: "omniroute-search", version: "1.0.0" });
|
||||||
|
|
||||||
|
server.registerTool(
|
||||||
|
"search",
|
||||||
|
{
|
||||||
|
description: "Web/news search via OmniRoute's /v1/search endpoint.",
|
||||||
|
inputSchema: { query: z.string().describe("Search query") },
|
||||||
|
},
|
||||||
|
async ({ query }) => {
|
||||||
|
const res = await fetch(`${BASE_URL}/v1/search`, {
|
||||||
|
method: "POST",
|
||||||
|
headers: { "Content-Type": "application/json", Authorization: `Bearer ${API_KEY}` },
|
||||||
|
body: JSON.stringify({ query }),
|
||||||
|
});
|
||||||
|
const text = await res.text();
|
||||||
|
if (!res.ok) return { content: [{ type: "text", text: `HTTP ${res.status}: ${text}` }], isError: true };
|
||||||
|
return { content: [{ type: "text", text }] };
|
||||||
|
}
|
||||||
|
);
|
||||||
|
|
||||||
|
await server.connect(new StdioServerTransport());
|
||||||
|
```
|
||||||
|
|
||||||
|
Register it in `~/.qwen/settings.json`:
|
||||||
|
|
||||||
|
```json
|
||||||
|
{
|
||||||
|
"mcpServers": {
|
||||||
|
"omniroute-search": { "command": "node", "args": ["<path-to>/index.mjs"] }
|
||||||
|
},
|
||||||
|
"tools": { "webSearch": { "enabled": false } }
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
It reuses the same `OMNIROUTE_API_KEY` env var as the model providers above — the virtual key needs search permission in OmniRoute, not just chat-completions.
|
||||||
|
|
||||||
|
## Auto Mode tuning
|
||||||
|
|
||||||
|
Auto Mode's action classifier calls the fast model above — its own request can queue behind other stack traffic before the fast llama-server instance is warm, so the default classifier timeout is worth raising. And since this stack is a single trusted local proxy, it's reasonable to pre-approve requests to it rather than confirm every call:
|
||||||
|
|
||||||
|
```json
|
||||||
|
{
|
||||||
|
"permissions": {
|
||||||
|
"autoMode": {
|
||||||
|
"classifier": { "timeouts": { "stage1Ms": 600000 } },
|
||||||
|
"hints": { "allow": ["Requests to proxy-ai.home, my own local omniroute model proxy"] }
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
`hints.allow` entries are free-text descriptions the classifier matches against, not exact strings — capped at 150 entries/200 chars each (see the `LLAMA_FAST_CTX_SIZE` note above for why that ceiling matters).
|
||||||
|
|
||||||
|
Everything else in `~/.qwen/settings.json` (`hooks`, `security.auth`'s underlying tooling, editor prefs) is per-machine, not part of pointing at this stack — don't copy it wholesale between machines.
|
||||||
@@ -4,7 +4,7 @@ This stack has no chat UI — every client is a coding CLI reaching the AI gatew
|
|||||||
|
|
||||||
## llama.cpp's raw API stays LAN-only — deliberately
|
## llama.cpp's raw API stays LAN-only — deliberately
|
||||||
|
|
||||||
The inference API (port `${LLAMA_PORT:-8080}`) is **not** registered in NPM and is **not** reachable externally. It has no authentication of its own — putting it on the public internet would mean an unauthenticated inference endpoint. Coding-agent CLIs (Claude Code, Kimi, OpenCode — see `docs/coding-cli-setup.md`) don't reach it directly at all now; they go through the gateway below, same as everything else.
|
The inference API (port `${LLAMA_PORT:-8080}`) is **not** registered in NPM and is **not** reachable externally. It has no authentication of its own — putting it on the public internet would mean an unauthenticated inference endpoint. Coding-agent CLIs (Claude Code, Kimi, OpenCode, Qwen Code — see `docs/coding-cli-setup/`) don't reach it directly at all now; they go through the gateway below, same as everything else.
|
||||||
|
|
||||||
If you later want external CLI access too, that's a deliberate scope change — see the map ([issue #1](https://git.arthurerlich.de/haylan/LLM-Server/issues/1)) before doing it, since it changes the security posture.
|
If you later want external CLI access too, that's a deliberate scope change — see the map ([issue #1](https://git.arthurerlich.de/haylan/LLM-Server/issues/1)) before doing it, since it changes the security posture.
|
||||||
|
|
||||||
|
|||||||
Reference in New Issue
Block a user