Generate a chat completion from an OpenAI chat-completions-shaped request. The completion is produced by the host agent's own model: direct MCP sampling on pre-2026-07-28 connections, an MRTR InputRequiredResult carrying the request payload on 2026-07-28+. When output_schema is given, the request carries exactly one required tool whose input schema is that schema, and the validated structured completion is returned as JSON text. When tools is given, the tool definitions are forwarded to the host's model with tool_choice, but only if the host advertised the sampling.tools capability: otherwise the tools are dropped (a warning is logged server-side) and the call degrades to a plain-text completion. When the host answers by calling tools, the returned text is JSON: {"text": "...", "tool_calls": [{"id": "...", "input": {...}, "name": "..."}]} (keys sorted). A plain-text host answer returns text as before. tools and output_schema cannot be combined. An optional persona {name, input_schema, output_schema} registers a per-call contract: the request arguments are validated against input_schema before anything is sent, and the host's completion text must parse as JSON validating against output_schema. The server is stateless and keeps no persona registry. persona.output_schema and output_schema cannot be combined. Session cursors: the server keeps no session state. To continue a conversation, pass the accumulated history back in and the previous turn's cursor as previous_response_id: '' starts a cursor chain (nothing verified), 'cur_<64 hex>' continues a verified chain (checked against the history passed back; mismatch fails loud naming expected vs received), 'resp_<32 hex>' (a responses_create response id) anchors a new chain at the passed history. When previous_response_id is given, the completion returns as JSON '{"completion": "", "response_id": "cur_<...>"}' instead of plain text; when omitted the tool returns plain text exactly as before. The cursor is 'cur_' + sha256 over canonical JSON '{"model","tool","transcript"}' (sorted keys, compact separators), the transcript being the conversation through the assistant turn, so the caller can recompute it. Re-supply previous_response_id on the MRTR retry round on 2026-07-28+ connections, like persona. No API key is used or needed: the connected agent is the model.

Generate a completion from an Anthropic messages-shaped request. The completion is produced by the host agent's own model: direct MCP sampling on pre-2026-07-28 connections, an MRTR InputRequiredResult carrying the request payload on 2026-07-28+. When output_schema is given, the request carries exactly one required tool whose input schema is that schema, and the validated structured completion is returned as JSON text. When tools is given, the tool definitions are forwarded to the host's model with tool_choice, but only if the host advertised the sampling.tools capability: otherwise the tools are dropped (a warning is logged server-side) and the call degrades to a plain-text completion. When the host answers by calling tools, the returned text is JSON: {"text": "...", "tool_calls": [{"id": "...", "input": {...}, "name": "..."}]} (keys sorted). A plain-text host answer returns text as before. tools and output_schema cannot be combined. An optional persona {name, input_schema, output_schema} registers a per-call contract: the request arguments are validated against input_schema before anything is sent, and the host's completion text must parse as JSON validating against output_schema. The server is stateless and keeps no persona registry. persona.output_schema and output_schema cannot be combined. Session cursors: the server keeps no session state. To continue a conversation, pass the accumulated history back in and the previous turn's cursor as previous_response_id: '' starts a cursor chain (nothing verified), 'cur_<64 hex>' continues a verified chain (checked against the history passed back; mismatch fails loud naming expected vs received), 'resp_<32 hex>' (a responses_create response id) anchors a new chain at the passed history. When previous_response_id is given, the completion returns as JSON '{"completion": "", "response_id": "cur_<...>"}' instead of plain text; when omitted the tool returns plain text exactly as before. The cursor is 'cur_' + sha256 over canonical JSON '{"model","tool","transcript"}' (sorted keys, compact separators), the transcript being the conversation through the assistant turn, so the caller can recompute it. Re-supply previous_response_id on the MRTR retry round on 2026-07-28+ connections, like persona. No API key is used or needed: the connected agent is the model.

Generate a completion from an OpenAI Responses-shaped request, returning the Responses API response object. The completion is produced by the host agent's own model: direct MCP sampling on pre-2026-07-28 connections, an MRTR InputRequiredResult carrying the request payload on 2026-07-28+. input is a string or a list of input items; each item is {role, content} with role user, assistant, system, or developer, and content a string or a list of text parts ({type: 'input_text'|'text', input_text: '...'}). A string input becomes one user message. System and developer items fold into the system prompt alongside instructions, which leads. Non-text parts are rejected. max_tokens is named for uniformity with the other tools (the Responses API calls it max_output_tokens); same meaning. When output_schema is given, the request carries exactly one required tool whose input schema is that schema, and the validated structured completion is returned as JSON text inside the output_text. When tools is given, the tool definitions are forwarded to the host's model with tool_choice, but only if the host advertised the sampling.tools capability: otherwise the tools are dropped (a warning is logged server-side) and the call degrades to a plain-text completion. When the host answers by calling tools, the output array carries the accompanying text as a message item (omitted when empty) followed by one function_call item per call: {type: 'function_call', id: 'fc_', call_id: '', name: '...', arguments: ''}; the host's tool-call id maps to call_id, the object id is derived from it. A plain-text host answer returns a single message item. tools and output_schema cannot be combined. An optional persona {name, input_schema, output_schema} registers a per-call contract: the request arguments are validated against input_schema before anything is sent, and the host's completion text must parse as JSON validating against output_schema. The server is stateless and keeps no persona registry. persona.output_schema and output_schema cannot be combined. Session cursors: the server keeps no session state. To continue a conversation, pass the accumulated history back in input and the previous turn's response id as previous_response_id: '' starts a cursor chain (nothing verified), 'cur_<64 hex>' continues a verified chain (checked against the history passed back; mismatch fails loud naming expected vs received), 'resp_<32 hex>' (a previous response id) anchors a new chain at the passed history. The response object's id field IS the cursor: when previous_response_id is given it carries the 'cur_' tamper-evident commitment, when omitted it stays the opaque 'resp_' unique id exactly as before. The cursor is 'cur_' + sha256 over canonical JSON '{"model","tool","transcript"}' (sorted keys, compact separators), the transcript being the conversation through the assistant turn, so the caller can recompute it. Re-supply previous_response_id on the MRTR retry round on 2026-07-28+ connections, like persona. No API key is used or needed: the connected agent is the model.

Generate a raw text completion from a plain prompt string. The prompt is sent as a single user sampling message with no system prompt and no chat formatting: raw prompt in, completion text out, produced by the host agent's own model. Same model hint and parameters as the other completion tools, minus any chat or tool features: no tools, no output_schema, no tool_choice. It does accept the optional persona contract ({name, input_schema, output_schema}): request/response validation only, never a sampling feature, so the thin path stays zero overhead. Session cursors: the server keeps no session state. previous_response_id '' mints a cursor over the current prompt and completion, returned as JSON '{"completion": "", "response_id": "cur_<...>"}' instead of plain text; when omitted the tool returns plain text exactly as before. generate has no structured history channel (the prompt is the only input), so a 'cur_' cursor can never be verified here: passing one fails loud instead of silently skipping the check. The cursor is 'cur_' + sha256 over canonical JSON '{"model","tool","transcript"}' (sorted keys, compact separators) with the transcript [{"role": "user", "content": }, {"role": "assistant", "content": }], so the caller can recompute it. No API key is used or needed: the connected agent is the model.

List the models this server offers. There is exactly one: agent-default, an alias for the host agent's own model.