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Owns the tool-call loop, the session model, and the one shared event reducer. Works in browser and Node.","maintainers":[{"name":"kimgish-adia","email":"kim.granlund@adia.ai"}],"readme":"# `@adia-ai/agent`\n\nComposable chat-agent harness. Assemble an agent from four declared parts —\n**prompt layers**, **tools**, **workflows**, **resources** — on top of\n[`@adia-ai/llm`](../core/README.md). The package owns the tool-call loop, the\nserializable `Session`, and the one shared event reducer; it renders nothing\n(pair it with `web-modules/chat` or any UI).\n\nThe contracts are validated by a real consumer: the A2UI generation pipeline\nruns on them ([gh#664](https://github.com/adiahealth/gen-ui-kit/issues/664))\n— its engine escalation ladder is a workflow\n(`packages/gen-ui/a2ui/compose/strategies/cascade.js`), its corpus reads are\ntool-mode resources, and its refiner is a defined tool. What that migration\ncould not express became this package's hardening pass\n([gh#665](https://github.com/adiahealth/gen-ui-kit/issues/665)): workflow\nverdicts, exit gates, same-step retry, a services channel, guardrails,\ntracing, session memory, and partial tool-input deltas.\n\nDeferred, with reason: **`@genui/adia-producer`'s bridge keeps its own loop.**\nThe retry seam covers per-tier round budgets, but the bridge also owns a\nturn-wide `maxProviderCallsPerTurn` budget spanning steps, a genui `TurnTrace`,\nand `OrchestrationExhaustedError` semantics that come from `@genui/producer`\n— none of which this package models, and modelling them here would import\nanother package's orchestration vocabulary into a general harness.\n\n## Install\n\n```bash\nnpm install @adia-ai/agent\n```\n\n## Usage\n\n```js\nimport {\n createAgent, promptLayer, defineTool, defineWorkflow,\n defineResource, reduce,\n} from '@adia-ai/agent';\n\nconst agent = createAgent({\n llm: { model: 'claude-sonnet-4-6', proxyUrl: '/api/chat' },\n prompt: [\n promptLayer('identity', 'You are the AdiaUI copilot.', { cache: true }),\n promptLayer('context', () => new Date().toISOString()), // per-turn, never cached\n ],\n tools: [\n defineTool({\n name: 'search_patients',\n description: 'Find a patient by name or MRN',\n inputSchema: { type: 'object', properties: { q: { type: 'string' } }, required: ['q'] },\n execute: async ({ q }) => lookup(q), // or endpoint: { url, method }\n }),\n ],\n workflows: [\n defineWorkflow('generate', [\n { name: 'zettel', run: zettel, until: r => r.strategy === 'composition-match' },\n { name: 'free-form', run: freeForm, until: async r => r.ok && await passesGate(r) },\n { name: 'monolithic', run: monolithic }, // terminal — always accepted\n ]),\n ],\n resources: [\n defineResource({ name: 'visit-summary', mode: 'attach', get: fetchSummary }), // app-controlled\n defineResource({ // model-controlled\n name: 'care-plan', mode: 'tool',\n inputSchema: { type: 'object', properties: { section: { type: 'string' } } },\n get: ({ section }) => readPlan(section), // non-string returns are JSON-serialized\n }),\n ],\n maxToolRounds: 8,\n});\n\nlet session = agent.createSession(); // plain JSON-safe object — you own persistence\nfor await (const event of agent.send(session, 'weather in Oslo?')) {\n session = reduce(session, event); // the one reducer — the single session writer\n render(event); // text | thinking | tool_use | tool_result | done | error\n}\nawait agent.run(session, 'generate', { intent });\n```\n\n## The four parts\n\n| Part | Declared with | What the harness does |\n|---|---|---|\n| Prompt | `promptLayer(name, text, {cache})` | Static layers first, `cache_control` on the last cached one (Anthropic block array; joined string elsewhere) |\n| Tools | `defineTool({name, inputSchema, execute\\|endpoint})` | JSON-schema check at the call boundary, then the execute-and-feed-back loop (bounded by `maxToolRounds`) |\n| Workflows | `defineWorkflow(name, steps, {exitGate})` | Sequential cascade — `until` accepts, absent `until` is terminal, `when` skips, `retry` re-runs the same step |\n| Resources | `defineResource({name, mode, inputSchema, get})` | `attach` → dynamic prompt layer each turn; `tool` → a read-only `read_` tool taking `inputSchema`'s arguments |\n\n**The attach envelope contains what it wraps.** An attach-mode resource\ninlines somebody else's bytes into the system prompt, so a body containing\n`` would otherwise close its own envelope and everything after it\nwould read as prompt — a file, a database row, or an MCP server could write\ninstructions into the system prompt that way. `attachLayers` escapes the two\nstructural sequences (` r.messages.length`)\nalready did. Only an object with a boolean `accepted` is treated as a verdict.\nThe reason lands on the attempt, so a consumer renders why the ladder climbed\ninstead of recomputing it:\n\n```js\nuntil: (r) => r.messages.length\n ? { accepted: true }\n : { accepted: false, reason: 'no-emission', detail: 'engine emitted nothing' },\n```\n\n**Exit gate.** The check every step's result must pass is declared once on\nthe workflow instead of restated in every `until`:\n\n```js\ndefineWorkflow('generate', steps, {\n exitGate: async (result, ctx) => await validatesAgainstCatalog(result, ctx.input),\n});\n```\n\nIt runs after a step's own `until` accepted, and a rejection falls the ladder\nthrough exactly like an `until` rejection (the attempt carries\n`gateRejected: true` and `reason: 'gate-rejected'`). A **terminal step — one\nwith no `until` — is not exit-gated**: the floor of a ladder is the answer of\nlast resort. Give the last step `until: () => true` if you want it gated too.\n\n**Same-step retry.** A step may retry against its own rejection before\nfalling through:\n\n```js\n{ name: 'repair', retry: { maxRounds: 3 },\n run: (ctx) => generate(ctx.input, ctx.feedback), // ctx.round is 1-based\n until: (r) => r.valid ? { accepted: true } : { accepted: false, reason: 'invalid', data: r.errors } }\n```\n\n`ctx.feedback` is the previous round's verdict; each round is its own entry\nin `attempts`. `maxRounds` counts total runs (2 = one retry). A workflow\n**exit-gate** rejection consumes a round exactly like an `until` rejection,\nand reaches the next round as `ctx.feedback` carrying `gateRejected: true` —\nso a repair round can tell the gate's objection from the step's own.\n\n**Services.** `ctx.input` is what the run is ABOUT; `ctx.services` is what it\nmay USE. Declare them on the agent, per run, or both (per-run wins):\n\n```js\ncreateAgent({ …, services: { llmAdapter, store } });\nawait agent.run(session, 'generate', { intent }, { services: { llmAdapter: turnAdapter } });\n```\n\nServices used to ride `input`, and still may — nothing breaks — but a new\nconsumer should keep `input` the subject and put dependencies here.\n\n## Events\n\n`agent.send()` yields the `AgentEvent` union; `reduce(session, event)` folds it.\nEvery failure inside a tool call becomes an `isError` `tool_result` the model\ncan react to — the loop throws for nothing tool-shaped.\n\n```\n{ type:'message', role:'user', text }\n{ type:'text', text, snapshot } · { type:'thinking', text }\n{ type:'tool_input_delta', id, name, partial, snapshot }\n{ type:'tool_use', id, name, input } · { type:'tool_result', id, name, output, isError? }\n{ type:'step', workflow, step, data? }\n{ type:'progress', stage } · { type:'surface', surfaceId, line }\n{ type:'guardrail', name, target, action, reason?, toolName?, toolUseId? }\n{ type:'trace', record } · { type:'memory', patch }\n{ type:'done', text, usage, stopReason } · { type:'error', error }\n```\n\n`stopReason` passes through raw from the provider; the loop adds exactly two\nsynthetic values — `max_tool_rounds` when the bound is hit, and\n`guardrail_denied` when an output guardrail vetoed the turn.\n\n`thinking` / `step` / `error` / `progress` / `surface` / `tool_input_delta` /\n`guardrail` / `trace` are render-only — `reduce()` folds them into no session\nchange. `memory` is the one new event that DOES change the session.\n\n### Partial tool input\n\nWhile a provider streams a tool call's arguments, each fragment surfaces as\n`tool_input_delta` (Anthropic `input_json_delta`, OpenAI partial argument\nstrings; Gemini streams complete calls and emits none). `snapshot` is the raw\nJSON so far — **incomplete and unparseable until the matching `tool_use`\narrives**. Render progress from it; execute only on `tool_use`.\n\n### Progress events (CHAT-HARNESS law 3)\n\n`progress` carries a CLOSED, code-owned `stage`: one of `PROGRESS_STAGES`\n(`'sent' | 'started' | 'reasoning' | 'content' | 'validating' | 'retry' |\n'tool' | 'done'`), exported alongside the `isProgressStage()` guard so any\nreader — a deserialized wire message, a stored transcript — can drop an\nout-of-vocabulary value instead of rendering it. **Never render `stage` as\nlabel text directly**; a UI's label table is code-owned and keyed on these\nstrings, never on model output (the honesty guard).\n\nThe loop today emits only the stages it genuinely reaches — `sent` before\nevery provider call, `retry` before a call that repeats a round after tool\nfeedback, `tool` before each tool executes, `done` right before the\nterminal `done` event. `started` / `reasoning` / `content` / `validating`\nare declared in the vocabulary for future producers (the L1 loop doesn't\nindependently observe those signals today, so it doesn't invent an\nemission point for them).\n\n### Surface events\n\n`{ type:'surface', surfaceId, line }` carries one generative-UI wire line\nfor an envelope-keyed surface. This package only carries the event kind on\nthe union — turning it into a rendered surface is L3's job\n(`web-modules/chat`, WCH-4), never this package's.\n\n### Framed client-message turns (CHAT-HARNESS law 6)\n\nA surface action, a function/tool result, or a validation rejection\nre-enters the agent as a *turn* — never a raw data blob — through one\nframing function:\n\n```js\nimport { frameClientMessage, shouldRunTurn } from '@adia-ai/agent';\n\nconst msg = { kind: 'surfaceAction', surfaceId: 'sf_1', action: 'submit', context: { formId: 'f1' } };\nif (shouldRunTurn(msg)) {\n for await (const event of agent.sendClientMessage(session, msg)) { /* … */ }\n}\n```\n\n`ClientMessage` arms and their framed wording:\n\n| Kind | Framed turn |\n|---|---|\n| `surfaceAction` | `The user triggered the ${action} action on surface ${surfaceId} with context ${JSON(context)}.` |\n| `functionResult` | `The function ${call} returned: ${JSON(value)}.` |\n| `validationRejection` | `The previous surface was rejected (${code}): ${message}. Emit a corrected surface.` |\n| `dataModelUpdate`, `ack` | silent-apply — `shouldRunTurn` returns `false`; `frameClientMessage` **throws** if called on one directly |\n\n`agent.sendClientMessage(session, msg, opts?)` is the wired entry: it gates\nwith `shouldRunTurn`, frames with `frameClientMessage`, then runs the same\nloop as `agent.send()`. A silent-apply message yields nothing and never\ntouches the provider — the caller already applied it directly (e.g. to a\nsurface's data-model store).\n\n## MCP-client mode\n\nPoint the agent at an [MCP](https://modelcontextprotocol.io) server and its\ntools and resources join the declared ones. Nothing downstream knows the\ndifference: the calls go through the guardrail chain, the tracer records the\nexecutions, `checkInput` runs at the boundary, a failure comes back as an\n`isError` tool_result. MCP is a *source* of tools, not a second kind of tool.\n\n```js\nimport { createAgent, mcpServer } from '@adia-ai/agent';\n\nconst agent = createAgent({\n llm: { model: 'claude-sonnet-4-6', proxyUrl: '/api/chat' },\n integrations: [\n mcpServer({\n url: 'https://mcp.example.com/mcp',\n headers: { authorization: `Bearer ${token}` }, // v1 auth: headers only\n prefix: 'wiki_', // namespace its tool names\n tools: ['search', 'fetch'], // allowlist; absent = everything it lists\n maxTools: 24, // loud cap, default MAX_INTEGRATIONS (16)\n attach: ['style-guide'], // resources to inline every turn\n timeoutMs: 60_000, // per request; 0 disables (default 60s)\n }),\n ],\n});\n\nconst report = await agent.connect(); // optional — otherwise the first turn does it\n// [{ url, serverInfo, protocolVersion, tools: [...], resources: [{name, uri, mode}] }]\nawait agent.close(); // ends the MCP session(s)\n```\n\n**Connection is lazy and memoized.** `createAgent` is synchronous and a\nconstructor that reaches the network is a constructor that can fail, so the\nhandshake happens on the first turn — or eagerly on `agent.connect()`, which\nreturns what each server offered. `agent.tools` lists the declared set until\nthe sources connect, and everything afterwards. A failed connect is not\ncached as a verdict: the next turn tries again.\n\n**Failure is loud where it must be and model-visible where it can be.**\n\n| When | What happens |\n|---|---|\n| Server unreachable, non-2xx, unspeakable protocol version, `tools/list` fails | `McpConnectionError` **thrown** out of `connect()` / `send()`. An infrastructure fault the model cannot work around — the same class as a missing `@adia-ai/llm`, never a silent empty tool list |\n| Two sources (or a source and a declared tool) contribute the same tool name | `McpConnectionError` at connect — set a `prefix` |\n| More tools listed than `maxTools` | `McpConnectionError` naming the count. `resolveIntegrations`' cap silently drops the overflow; this one refuses to serve two thirds of a server without saying so |\n| A `tools/call` fails, times out, is aborted, or the server goes away mid-session | `isError` tool_result (`McpCallError`'s message). The model sees it and the loop continues |\n| The server answers `isError: true` | Same — the error text reaches the model as an error, never as prose that reads like an answer |\n| An **attach-mode** resource's `resources/read` fails mid-session | `McpCallError` **thrown out of `send()`** during prompt assembly. There is no turn to degrade into: the prompt the model was going to see is the thing that could not be built |\n\n**Nothing waits forever.** Every round trip carries `timeoutMs` (default\n`DEFAULT_MCP_TIMEOUT_MS`, 60 s), covering both the fetch and the wait for the\nanswer to arrive on an SSE body — a server that accepts a POST and then says\nnothing is a real failure mode and is otherwise indistinguishable from a hang.\nThe default has to clear the slowest legitimate answer, and for MCP that is a\ntool that thinks (the in-repo a2ui server runs an LLM inside `tools/call`), so\nit is generous rather than handshake-sized; raise it per source for a slower\nserver, or set `0` if you own the clock through `signal`. A timeout during\nconnect throws; a timeout during a call reaches the model like any other call\nfailure.\n\n**Abort is plumbed end to end.** `agent.send(session, text, { signal })` puts\nthat signal on the turn's `ctx`, and an MCP `tools/call` or `resources/read`\ncarries it to the wire — a user who cancels mid-answer cancels the server call\ninstead of leaving it running and ignored. Aborting the turn that happens to\nbe doing the connect cancels the handshake too; the signal does *not* stay\nattached to the resulting connection, so a later turn is unaffected.\n\n**Failures leave nothing open.** If a source fails after its handshake\nsucceeded, its session is closed before the error propagates; if the second of\ntwo sources fails, the first one's session is closed too. `close()` during an\nin-flight connect wins — the late arrival closes what it opened and rejects\nwith \"the agent was closed while connecting\" rather than quietly reinstalling\nitself. `close()` ends the sessions but does not spend the agent: a later\n`send()` reconnects.\n\n**Schemas pass through verbatim.** A server's JSON Schema reaches the model\nunmodified, including keywords `checkInput` cannot enforce. The server is the\nauthoritative validator (its rejection returns as an error result), so a\nfaithful copy of a contract someone else owns beats a locally-enforceable\nsubset that describes the tool less accurately. The manifest states this with\nthe new `IntegrationManifest.validation: 'remote'` rather than leaving it\nimplied; `'local'` (the default, and every hand-declared integration) keeps\nthe strict registration allowlist, because there the only validator is us.\n\n**Transport is streamable-HTTP, hand-rolled, no new dependency.**\n`@modelcontextprotocol/sdk` brings a server framework, stdio/websocket\ntransports, an auth stack and zod for a client subset that is six JSON-RPC\nmethods over one POST endpoint; this package stays dependency-free apart from\n`@adia-ai/llm`. The SDK still belongs in `packages/gen-ui/a2ui/mcp`, which *serves*.\n**stdio is not supported** — it needs a child process, so it is Node-only by\nconstruction and no in-repo consumer wants it; a `mcpServerStdio()` Node entry\npoint is the shape to add if one does.\n\n**In a browser, the server must be reachable by `fetch`** — meaning it sends\nCORS headers for your origin, or you put it behind the same proxy you already\nroute `@adia-ai/llm` through (`proxyUrl`) and point `url` at that path. This\npackage deliberately ships no proxy: a server-side pass-through is the host\napp's, and forwarding `mcp-session-id` and `mcp-protocol-version` both ways is\nall it has to do. `fetchImpl` is the injection seam (tests use it for the\nin-process server; a host can use it to route through its own client). The\nin-repo `packages/gen-ui/a2ui/mcp` server sends no CORS headers today, so reaching\nit from a page means proxying it; from Node it works directly.\n\n### MCP resources, and the gh#671 ruling\n\nA server's resources map onto the two existing modes and **no new one**:\n\n- **`tool` (default)** — the harness cannot know which of a remote server's\n resources belong in every prompt, and attaching all of them grows the\n system prompt without bound. So each becomes a `read_` tool.\n- **`attach`** — the APP promotes specific resources by name or URI\n (`attach: ['style-guide']`), and those are fetched and inlined every turn.\n\nThat is exactly MCP's own application-controlled vs. model-controlled\ndistinction, expressed with the modes the harness already has.\n\n[gh#671](https://github.com/adiahealth/gen-ui-kit/issues/671) asked whether\nengine-internal retrieval (zettel and free-form reading corpus content on\ntheir own schedule inside a workflow step's `run`) needs a third mode —\n`'internal'`. **Ruled no, and W3 did not need one either.** A resource mode\nis a declaration of *who invokes it and when*, and both existing modes name a\nparty the harness mediates: `attach` = the harness fetches, `tool` = the model\nasks. `'internal'` would name \"engine code, whenever it likes\", which is not\na mediation — it is a function call. Declaring it would put into the resource\ncontract a thing the model can never call and the harness never fetches,\nbuying nothing but a second way to spell `await`. The two things the issue\nactually wanted are already contracts: the dependency is declared through\n**`services`** (W2), and the invocation is visible through **`ctx.trace`** in\nthe `workflow` phase. Server-pushed MCP resources do not need it either —\nevery read is either app-scheduled or model-asked.\n\n**Everything a server returns is DATA.** A tool description, a resource body,\nan error string — a description that reads like an instruction is still a\ndescription. It lands in the tool spec or inside a `` envelope and\ndecides nothing about what the harness does. Guardrails are how you constrain\nwhat a remote tool may actually do.\n\n## Guardrails\n\nThe deterministic layer between the model and the world — code that runs\noutside the model's context and cannot be talked past. Declared once at\n`createAgent`, two attach points, both optional:\n\n```js\nimport { defineGuardrail } from '@adia-ai/agent';\n\ncreateAgent({\n llm: { … },\n guardrails: [\n defineGuardrail({\n name: 'scope-to-tenant',\n // Before the call executes: allow (or return nothing), rewrite, deny.\n toolCall: (call, ctx) => ({ action: 'rewrite', input: { ...call.input, tenantId } }),\n }),\n defineGuardrail({\n name: 'redact',\n // On the model's completed text for the round.\n output: (text) => ({ action: 'rewrite', text: text.replace(SSN, '[redacted]') }),\n }),\n ],\n});\n```\n\nGuardrails run in declaration order and chain: a rewrite feeds the next one,\nthe first `deny` short-circuits.\n\n- **Tool-call deny** → the model gets an `isError` tool_result naming the\n guardrail and its reason, and the loop continues. Nothing executes.\n- **Output deny** → the turn ends with no assistant text and\n `stopReason: 'guardrail_denied'`; the reason rides the `guardrail` event.\n- **Output rewrite** → the rewritten text is what `done` carries and what the\n transcript keeps. A final `{ text: '', snapshot: }` event\n follows, and a denial sends `{ text: '', snapshot: '' }`.\n **A UI that folds `text` events by APPENDING the delta must treat an empty\n delta as a replace from `snapshot`, or it will keep showing the original\n while the transcript says otherwise** — `web-modules/chat`'s\n `wireAgentEvents` does exactly this via `chatShell.setStreamedText()`.\n\nTwo scope limits worth knowing before you rely on the output chain:\n\n- **Output guardrails see the text of the round that ENDS the turn.** Text\n from a round that also called tools is intermediate reasoning the model\n supersedes, and denying it has no coherent answer for the tool calls\n emitted beside it — so it passes through unguarded and reaches the\n transcript. Put the check on `toolCall` if what matters is what the model\n is about to DO.\n- **A guardrail may `ctx.remember(patch)`** — it receives the same context a\n tool does. The write is drained as a `memory` event before `done`, never\n dropped.\n\n`onToolCall` is now exactly one guardrail of the `toolCall` kind: it still\nworks, still denies with its original wording, and runs ahead of the declared\nchain.\n\nA guardrail decides from the call and the harness's own context. Text\nproduced by a model, a tool, or a resource is DATA — a guardrail that lets\nsuch text talk it into allowing something is not a guardrail.\n\n## Tracing\n\nOff by default. `trace: true` uses the console sink; a function IS the sink.\nEvery record is also a `trace` event on the stream:\n\n```js\nconst agent = createAgent({ llm: { … }, trace: (record) => myLogger.debug(record) });\n// { phase, name, at, sessionId?, data? }\n```\n\n`phase` is the closed vocabulary (`TRACE_PHASES`, guarded by\n`isTracePhase()`): `prompt` (assembly: layer count, cached blocks, chars),\n`request` / `response` (per provider call: round, model, tool count, stop\nreason, ms), `tool` (per execution: name, id, ms, isError), `workflow`\n(`step:start` / `step:verdict` with the verdict reason), `guardrail` (each\nnon-allow decision), `usage` (the turn's token totals), `mcp` (`connect` /\n`connect:failed` per source — a tool a server contributed then executes\nunder `tool` like any other). A sink that throws\nwarns and the turn continues. There is no exporter protocol and no\ndependency — an OpenTelemetry bridge is a caller-side function.\n\n## Session memory\n\nPer-session, JSON-safe, and written ONLY through `reduce()` — the same\nsingle-writer rule the messages obey:\n\n```js\nimport { remember } from '@adia-ai/agent';\n\nsession = reduce(session, remember({ patientId: 'p_7', tone: 'brief' }));\nsession.memory; // { patientId: 'p_7', tone: 'brief' }\nsession = reduce(session, remember({ tone: null })); // null deletes the key\n```\n\nA tool writes by ASKING: `ctx.remember(patch)` queues the patch, and the loop\nyields it as a `memory` event right after that tool's result — and drains\nanything still queued before `done`, so a final-round tool's write (or a\nguardrail's, which shares the context) is never dropped. The asker never\ntouches state.\n\nA patch that would not survive `JSON.stringify` → `JSON.parse` unchanged (a\nDate, a function, `undefined`, a class instance) throws at the write, naming\nthe path. Memory rides the Session through `toJSON`/`fromJSON`; **where that\nJSON is stored stays the caller's** — cross-session persistence is a seam\nthis package deliberately does not implement.\n\n## Byte-identity baseline (CHAT-HARNESS law 2)\n\nEvery axis on `AgentConfig` is optional, and absence must produce a\nbyte-identical request to before the axis existed. `baseline/prompt-\nequivalence.baseline.json` pins the exact `ChatOpts` a minimal config (one\nstatic prompt layer, nothing else) produces; `baseline.test.js` re-derives\nit every run and diffs byte-for-byte, including asserting no optional-axis\nkey (`tools`, `cache`, `signal`, …) appears at all — not even as an empty\narray.\n\nChanging a default that legitimately changes this output breaks the test\nuntil the baseline is deliberately regenerated:\n\n```bash\nnpm run build -w @adia-ai/agent\nnode scripts/build/regen-agent-baseline.mjs\n```\n\nNever hand-edit the JSON file — regenerate it, then diff-review the change\nlike any other pinned contract.\n\n## Testing\n\n`scriptClient(turns)` is a deterministic `LLMClient`: script `[{text, toolUse}]`\nturns and drive the whole loop keylessly. It also records every `ChatOpts` it\nwas called with (`client.calls`) so tests can assert what reached the wire.\n\n`mcp-fixture.mjs`'s `createTestMcpServer({ tools, resources, … })` is the\nsame idea for MCP: an in-process server behind a `fetch`-shaped function\n(`fetchImpl`), speaking real JSON-RPC with session headers, cursor\npagination, `isError` results, and either a JSON or an SSE response body —\nno network, no ports, no SDK.\n","readmeFilename":"README.md"}