# OrcaRouter (npm · @orcarouter/mcp)

Browse 160+ LLM models and live pricing — no API key needed for catalog. Add key to route chat.

- Trust score: 67/100 (medium)
- Change this week: +23
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-03

## Components

- npm · `@orcarouter/mcp`: 67/100 (this document), [markdown](https://verifymcp.io/servers/continuum-ai-corp-orcarouter-mcp/orcarouter-mcp.md), [page](https://verifymcp.io/servers/continuum-ai-corp-orcarouter-mcp/orcarouter-mcp)

## Channel facts

- Registry: `npm`
- Package: `@orcarouter/mcp`
- Version: `1.1.5`
- Transport: `stdio`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-08-03.

- **Supply Chain Security**: 86/100
  - No malware found by supply-chain analysis.
  - Only part of the dependency tree could be resolved (94 of 98), so this covers what we could see, not the whole tree.
  - No install/post-install scripts declared.
  - Only part of the dependency tree could be resolved (94 of 98), so this covers what we could see, not the whole tree.
- **Provenance & Transparency**: 45/100
  - Source repository is publicly reachable at the declared URL.
  - Provenance check failed: no build-provenance attestation is published.
  - Clear OSI-approved license (MIT).
  - Actively maintained (last published 68 days ago).
  - Disclosure check failed: no security disclosure policy was found in the source repository.
- **Schema Quality & AI Usability**: 66/100
  - AI-judged instruction clarity (excellent).
  - Context-footprint check failed: tool/resource definitions use about 802 tokens (~200/item across 4 items; 4 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 27/100
  - Stability observed for 8 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### Claude

```bash
claude mcp add continuum-ai-corp-orcarouter-mcp -- npx -y @orcarouter/mcp
```

### Codex

```bash
codex mcp add continuum-ai-corp-orcarouter-mcp -- npx -y @orcarouter/mcp
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "continuum-ai-corp-orcarouter-mcp": {
      "type": "local",
      "command": [
        "npx",
        "-y",
        "@orcarouter/mcp"
      ],
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add continuum-ai-corp-orcarouter-mcp --command npx --arg -y --arg @orcarouter/mcp
```

### Hermes

```yaml
mcp_servers:
  continuum-ai-corp-orcarouter-mcp:
    command: "npx"
    args: ["-y", "@orcarouter/mcp"]
```

### Other

```json
{
  "mcpServers": {
    "continuum-ai-corp-orcarouter-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@orcarouter/mcp"
      ]
    }
  }
}
```

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-08-03 (score 67, +1)

No change was recorded against any check on this day. Stability & Change Management went from 23 to 27. That category is still filling its 30-day observation window: 7 days of observed history at the previous scan, 8 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-08-02 (score 66, +47)

- [security regression] Provenance: unverified → fail
- [security improvement] Known CVEs: unverified → partial
- [security improvement] Install scripts: unverified → pass
- [security improvement] Malware scan: unverified → pass
- [functional regression] Tool coverage: 100 → unverified
- [functional improvement] Schema quality: unverified → excellent
- [functional improvement] License: unverified → pass
- [functional improvement] Dependency health: unverified → partial
- [functional improvement] Maintenance: unverified → pass
- [functional improvement] MCP protocol: unverified → pass
- [functional improvement] Stability: unverified → 0.23
- [functional] Licence: MIT

### 2026-07-31 (score 19, −5)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-30 (score 24, −20)

- [functional regression] Tool coverage: 100 → unverified
- [functional] First check of Schema quality: unverified

### 2026-07-27 (score 44)

First indexed and scored.

## MCP tools (4)

### `orcarouter_chat` (~337 tokens)

Send a single-turn chat request to OrcaRouter and return the assistant's response text. Default model is the workspace's auto-router. Use `orcarouter/<name>` for other routers or `<provider>/<model>` for direct calls. For OpenAI reasoning models (gpt-5/o1/o3/...), max_tokens is automatically routed to max_completion_tokens at the wire level. The optional `models` array sets a fallback chain — the primary `model` is tried first, then each entry on failure (5 entries total max, including the primary). Errors are returned as text content with isError:true; common cases include missing API key, rate limits, and upstream provider outages. Requires ORCAROUTER_API_KEY.

Input parameters:

- `max_tokens` (integer): Maximum tokens to generate (default 10000). Automatically translated to max_completion_tokens for OpenAI reasoning models.
- `model` (string): Model to call. Defaults to `orcarouter/auto` — your workspace's seeded auto-router. Use `orcarouter/<name>` for other workspace routers, or `<provider>/<model>` for direct upstream selection (e.g. `o…
- `models` (array): Optional fallback chain. Models are tried in order if the primary fails. Max 5 entries including the primary.
- `prompt` (string, required): User message to send (single-turn).
- `system_prompt` (string): Optional system prompt prepended to the conversation.
- `temperature` (number): Sampling temperature (default 0.7).

### `orcarouter_models_list` (~179 tokens)

List LLM models in the OrcaRouter catalog. Each entry includes id, name, description, owned_by, context_length, supported_endpoint_types, and pricing (both per-token and per-million tokens). Filter by `provider`, `capability`, or `min_context` — filters compose (all conditions must match) and are applied server-side. Discover valid provider ids first with orcarouter_providers_list. Returns the full catalog when called without filters. Read-only, no API key required.

Input parameters:

- `capability` (string): Filter to models supporting this capability.
- `min_context` (integer): Filter to models with context window at least this large (tokens).
- `provider` (string): Provider id (lowercase, e.g. 'openai', 'anthropic'). Get the full list via orcarouter_providers_list.

### `orcarouter_model_card` (~170 tokens)

Get detailed information about a single model — display name, long description, pricing (per-call and per-million tokens), context window, max output, modalities (input/output), supported endpoints, latency percentiles (p50/p95), and release date. Use this when you already know the model id and want full details; for browsing or filtering across many models use orcarouter_models_list instead. Returns isError:true with a clear hint when the id is not found. Read-only, no API key required.

Input parameters:

- `model` (string, required): Model ref in `provider/slug` form (e.g. `openai/gpt-4o-mini`, `anthropic/claude-haiku-4.5`). Use the exact `id` value returned by orcarouter_models_list.

### `orcarouter_providers_list` (~116 tokens)

List all model providers on OrcaRouter with their `provider_id`, human-readable `display_name`, `icon_url`, and `model_count`. Call this first to discover valid provider ids (e.g. 'openai', 'anthropic', 'google', 'qwen', 'deepseek') which you can then pass to orcarouter_models_list as the `provider` filter. Takes no parameters and returns the same list on every call until the deployment's catalog changes. Read-only, no API key required.

## Diagnostics

Captured diagnostic sections: Provenance, Dependencies. The full working is on the page: https://verifymcp.io/servers/continuum-ai-corp-orcarouter-mcp/orcarouter-mcp#diagnostics

## Score history

- 2026-08-03: 67
- 2026-08-02: 66
- 2026-08-01: 19
- 2026-07-31: 19
- 2026-07-30: 24
- 2026-07-28: 44
- 2026-07-27: 44

## Links

- npm package: https://www.npmjs.com/package/@orcarouter/mcp
- Socket report: https://socket.dev/npm/package/@orcarouter/mcp
- Repository: https://github.com/Continuum-AI-Corp/orcarouter-mcp-server
- Website: https://orcarouter.ai/
- Changelog RSS feed: https://verifymcp.io/servers/continuum-ai-corp-orcarouter-mcp/orcarouter-mcp/changelog.xml
- Changelog JSON feed: https://verifymcp.io/servers/continuum-ai-corp-orcarouter-mcp/orcarouter-mcp/changelog.json
- HTML version of this page: https://verifymcp.io/servers/continuum-ai-corp-orcarouter-mcp/orcarouter-mcp