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Nothing assumes a vendor.\n- **Runs fully local.** Qwen, Gemma, Llama and friends via Ollama, vLLM or llama-swap. Your content never leaves the machine. Read [Choosing a model](#choosing-a-model) first, since model size decides whether tool calling actually works.\n- **Per-tool permissions.** Grant `searchContent` without granting `sendEmail`, per role and per token.\n- **MCP built in.** Registers its tools onto Strapi's official `/mcp` endpoint for Claude Desktop and other clients.\n- **Extensible.** Other plugins contribute tools through a small service contract.\n- **Guardrails.** Prompt-injection screening on chat input.\n\n---\n\n## Overview\n\nStrapi holds unpublished drafts, customer records and internal documents. This plugin gives a model tools that read and write them, which makes the choice of where inference happens a real decision rather than a detail. You can keep all of it inside your own network.\n\nThere are exactly two surfaces:\n\n| Surface | Where | Authenticates with | Tools it can use |\n|---|---|---|---|\n| Admin chat | `/ai-sdk/*` in the admin panel | Logged-in admin session | What that admin's **role** grants |\n| MCP | Strapi's own `POST /mcp` | Admin API token | What that **token** grants |\n\nThe plugin does not serve `/mcp` itself. It registers its tools onto Strapi's built-in MCP server at boot.\n\nBuilt on the [Vercel AI SDK](https://ai-sdk.dev/).\n\n---\n\n## Installation\n\n**npm**\n\n```bash\nnpm install strapi-plugin-ai-sdk\n```\n\nRequires Strapi 5.47 or later, which is where the built-in MCP server appears.\n\n### 1. Configure the plugin\n\n```typescript\n// config/plugins.ts\nexport default ({ env }) => ({\n  'ai-sdk': {\n    enabled: true,\n    config: {\n      apiKey: env('ANTHROPIC_API_KEY'),\n      chatModel: env('ANTHROPIC_MODEL', 'claude-sonnet-5'),\n    },\n  },\n});\n```\n\n### 2. Enable Strapi's MCP server\n\n```typescript\n// config/server.ts\nexport default ({ env }) => ({\n  host: env('HOST', '0.0.0.0'),\n  port: env.int('PORT', 1337),\n  mcp: {\n    enabled: true,\n  },\n});\n```\n\n> This goes in `config/server.ts`, not `config/plugins.ts`. Without it the plugin registers no tool permissions at all, which leaves admin chat with no tools either, not just MCP.\n\n### 3. Build and start\n\n```bash\nnpx strapi build\nnpx strapi develop\n```\n\n### 4. Grant the tools\n\nOpen **Settings > Administration Panel > Roles**, pick a role, and tick the tools under each plugin's section. Super Admin is granted everything automatically; every other role starts with nothing.\n\nChat is now available at **AI Chat** in the admin sidebar.\n\n---\n\n## Bring your own model\n\nTwo providers cover four deployment shapes. The tools, permissions and MCP surface are identical in every case.\n\n| Deployment | Provider | Whose hardware | Content leaves your network |\n|---|---|---|---|\n| Frontier API | `anthropic` | Anthropic's | yes |\n| Hosted inference | `openai-compatible` | a vendor's | yes |\n| Self-hosted on cloud | `openai-compatible` | your rented GPU | not to a model vendor |\n| Local or LAN | `openai-compatible` | your machine | **no** |\n\n### Anthropic (default)\n\n```typescript\nconfig: {\n  apiKey: env('ANTHROPIC_API_KEY'),\n  chatModel: 'claude-sonnet-5',\n}\n```\n\nDo not set `temperature` here. Anthropic's newer models reject it outright, which fails every request.\n\n### Local models\n\nAnything speaking the OpenAI wire format. `baseURL` is required, `apiKey` is not.\n\n```typescript\nconfig: {\n  provider: 'openai-compatible',\n  baseURL: env('AI_BASE_URL', 'http://localhost:11434/v1'),\n  chatModel: env('AI_CHAT_MODEL', 'gemma4:26b'),\n}\n```\n\nThe model, your content and every tool result stay on the machine.\n\n### Self-hosted on cloud hardware\n\nIdentical configuration, different host:\n\n```typescript\nconfig: {\n  provider: 'openai-compatible',\n  baseURL: env('AI_BASE_URL', 'https://inference.example.internal/v1'),\n  apiKey: env('AI_API_KEY'),\n  chatModel: 'qwen3.6-35b',\n  temperature: 1,\n  topP: 0.95,\n}\n```\n\nThe weights are yours and no model vendor is involved. The chat header will not show the LOCAL badge, since that tracks network location rather than ownership.\n\n### Hosted inference vendors\n\nAny vendor exposing an OpenAI-compatible chat completions endpoint uses the same\nprovider. Take the base URL and model id from that vendor's own documentation:\n\n```typescript\nconfig: {\n  provider: 'openai-compatible',\n  baseURL: env('AI_BASE_URL'),   // from your vendor\n  apiKey: env('AI_API_KEY'),\n  chatModel: env('AI_CHAT_MODEL'),\n}\n```\n\nNot tested against any specific vendor. What the plugin requires is the wire\nformat, so anything that speaks it should work, but treat that as untested\nrather than promised.\n\n### Choosing a model\n\nRead this before you pick one. It is the difference between the plugin working\nand the plugin looking broken.\n\n**Size matters more than you expect.** This plugin does not just ask a model\nquestions. It hands the model tools and asks it to use them, often several in a\nrow: read a transcript, look up the schema, then write a record. Small models\ncan hold a conversation just fine and still fail completely at that. When they\nfail, they usually do not say so. They write \"I have saved the article\" and stop,\nhaving saved nothing.\n\nRough guidance from testing this plugin:\n\n| Size | What to expect |\n|---|---|\n| Under 7B | Will not reliably call tools at all |\n| 8B or so | Calls one tool, then tends to narrate the rest instead of doing it |\n| 14B | The first tier that completed a multi step write here, with `qwen3:14b` |\n| 26B to 35B | Varies by model family more than by size |\n| Frontier (Claude, and similar) | Handles the full chain and recovers from its own mistakes |\n\n**Watch the context window, not just the model.** This is the trap that costs\npeople the most time. A chat request from this plugin carries roughly 7,000\ntokens of instructions and tool definitions before your question is even read.\nOllama defaults to a 4,096 token window unless the model file sets `num_ctx`,\nso a large capable model can be silently truncated to less than it needs and\nappear to hang or ignore its tools. Check it:\n\n```bash\nollama show <model> | grep num_ctx     # blank means the 4,096 default applies\n```\n\nFix it by creating a variant with a real window. This reuses the existing\nlayers, so it does not duplicate the weights:\n\n```bash\nprintf 'FROM <model>\\nPARAMETER num_ctx 32768\\n' > Modelfile\nollama create <model>-32k -f Modelfile\n```\n\nNote that `maxOutputTokens` does not help here. That limits what the model\nwrites, not how much it can read.\n\n**Recovery is the real skill.** Writing content usually fails on the first try,\nbecause the model guesses a field value that your schema rejects. What separates\na model that works from one that does not is whether it reads the error and\ntries again. In one measured run Claude hit two separate errors, corrected both,\nand created the article on its fourth attempt. Smaller models spend their single\nattempt and give up.\n\nThese are the models actually exercised against this plugin, not a compatibility\nmatrix:\n\n| Model | Served by | Result on a transcript to article task |\n|---|---|---|\n| `claude-sonnet-5` | Anthropic | Completes it, recovers from failed writes. The default |\n| `qwen3:14b` | Ollama | Completes it, recovers from a failed write. Best local result |\n| `qwen3.6-35b` | llama-swap on a LAN box | Short tool chains work, needs generous output headroom |\n| `gemma4:26b` | Ollama | Reads and searches, did not complete the write |\n| `gemma4-kb` (8B) | Ollama | Fetches, then describes the save instead of doing it |\n| `llama3.2:3b` | Ollama | Does not call the tools |\n\nIf you want everything on your own hardware, that works. `qwen3:14b` with a\n32k window completed the same transcript to article task as the frontier model,\nincluding correcting a rejected write on its second attempt. Note that model\nfamily matters more than parameter count: a 26B model failed the task that this\n14B one passed.\n\nTwo more things that surprise people:\n\n- **Reasoning models need output headroom.** `qwen3.6-35b` spent 193 completion tokens reasoning before answering a one word question. At `maxOutputTokens: 30` it returned nothing at all, with no error.\n- **Local inference is slower.** Expect several times the latency of a frontier API. A 26B model can take minutes on a single step.\n\n### Registering your own provider\n\nAny [AI SDK provider](https://ai-sdk.dev/providers/ai-sdk-providers) works:\n\n```typescript\nimport { AIProvider } from 'strapi-plugin-ai-sdk/server/lib/ai-provider';\nimport { createMistral } from '@ai-sdk/mistral';\n\nAIProvider.registerProvider('mistral', ({ apiKey, baseURL }) => {\n  const provider = createMistral({ apiKey, baseURL });\n  return (modelId: string) => provider(modelId);\n});\n```\n\nThe two built-ins are registered through this same call, so nothing about them is privileged.\n\n---\n\n## Permissions\n\nEvery tool has its own admin action, named `plugin::<owning-plugin>.tool.<slug>`. Tools contributed by another plugin appear under that plugin's section, not this one's.\n\nThe same actions gate two different callers:\n\n- Granted on a **role**, they decide what that admin's chat can use.\n- Granted on an **admin token**, they decide what that token exposes over `/mcp`.\n\nAn ungranted tool is invisible rather than merely blocked. It never appears in `tools/list`, and the chat model is never offered it.\n---\n\n## Connecting an MCP client\n\nMint an admin token under **Settings > Administration Panel > Admin Tokens**, ticking the tools it should reach. Then point your client at `/mcp`:\n\n```json\n{\n  \"mcpServers\": {\n    \"strapi\": {\n      \"command\": \"npx\",\n      \"args\": [\"mcp-remote\", \"http://localhost:1337/mcp\", \"--header\", \"Authorization: Bearer YOUR_ADMIN_TOKEN\"]\n    }\n  }\n}\n```\n\nAdmin tokens, not content API tokens. They are different things in Strapi and only the former reaches `/mcp`.\n\n---\n\n## Available tools\n\nEight tools reach MCP:\n\n`listContentTypes`, `searchContent`, `findOneContent`, `aggregateContent`, `createContent`, `updateContent`, `uploadMedia`, `sendEmail`\n\nSix more are chat-only and need no grant, since they act on the calling admin's own data: `saveMemory`, `recallMemories`, `recallPublicMemories`, `saveNote`, `recallNotes`, `manageTask`.\n\n> See [docs/plugin-contract.md](./docs/plugin-contract.md) for the full contract, and [docs/architecture.md](./docs/architecture.md) for how tools are registered and permissioned.\n\n---\n\n## Extending with custom tools\n\nAnother plugin exposes an `ai-tools` service, and this plugin discovers it at boot:\n\n```typescript\n// your-plugin/server/src/services/ai-tools.ts\nimport { z } from 'zod';\n\nexport default () => ({\n  getTools() {\n    return [\n      {\n        name: 'analyzeContent',\n        description: 'Analyze content quality and suggest improvements',\n        schema: z.object({\n          documentId: z.string().describe('Document to analyze'),\n        }),\n        execute: async (args, strapi) => {\n          return { score: 85 };\n        },\n      },\n    ];\n  },\n});\n```\n\nThe tool's permission appears under your plugin's own section, and starts ungranted.\n\n> Full contract, namespacing rules and Zod requirements: [docs/plugin-contract.md](./docs/plugin-contract.md)\n\n---\n\n## Configuration reference\n\n| Option | Default | Notes |\n|---|---|---|\n| `apiKey` | | Provider API key. Not required for most local runtimes. |\n| `provider` | `anthropic` | `anthropic`, `openai-compatible`, or one you registered. |\n| `baseURL` | | Required for `openai-compatible`. |\n| `chatModel` | `claude-sonnet-5` | Model id. |\n| `systemPrompt` | | Supports a `{tools}` placeholder. |\n| `maxOutputTokens` | `8192` | Lower it with care on reasoning models. |\n| `maxConversationMessages` | `15` | History kept per request. |\n| `maxSteps` | `10` | Tool-call rounds per response. |\n| `temperature`, `topP`, `topK` | | Omitted unless set. Anthropic rejects `temperature`; `openai-compatible` drops `topK`. |\n| `guardrails` | enabled | See [docs/guardrails.md](./docs/guardrails.md). |\n\n---\n\n## Development\n\nChanges to admin code need two builds, not one:\n\n```bash\nnpm run build          # in the plugin\nnpx strapi build       # in the host app, easy to miss\nnpx strapi start\n```\n\nStrapi compiles plugin admin code into the host's own admin bundle. Rebuilding only the plugin updates the backend while the browser keeps serving the previously built admin, so admin changes appear to do nothing. Server-side changes need only the plugin build and a restart.\n\n```bash\nnpm run test:unit      # no Strapi needed\nnpm run test:ts:back\nnpm run test:ts:front\n```\n\n---\n\n## Documentation\n\n- [Architecture](./docs/architecture.md): system design, data flows, permission model\n- [Plugin contract](./docs/plugin-contract.md): the `ai-tools` service, tool definitions, namespacing\n- [Guardrails](./docs/guardrails.md): screening, patterns, and what is not covered\n\n---\n\n## License\n\n[MIT](./LICENSE)\n","readmeFilename":"README.md"}