mirror of
https://github.com/deepseek-ai/deepseek-harness
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149ab1bba4
When the client advertises clientCapabilities._meta.terminal_output (Zed), a
bash tool call now renders as a real TERMINAL card — a cwd header + the command
+ its output — instead of the plain ```console text block. Keeps agent-side
dsh-bash execution; rejects the spec's client-side terminal/create (which would
bypass sandbox/env-scrub/ownership/cwd). Matches what claude-agent-acp and
codex-acp do; wire contract verified against Zed's source.
- dsh-tools: a provider-neutral ToolTerminal shape ({ cwd?, output? }) on
ToolCallPresentation/ToolResultPresentation — a tool asks "render me as a
terminal"; no ACP types leak in.
- dsh-tool-bash: bash presentCall marks terminal (cwd from an explicit absolute
workdir, else left for the bridge to fill from the session cwd); presentResult
carries the output alongside the ```console fallback.
- dsh-acp: initialize reads/remembers the _meta.terminal_output capability;
streamSessionEventUpdate maps a terminal presentation to
content:[{type:'terminal',terminalId}] + _meta.terminal_info on the call and
_meta.terminal_output on the update WHEN capable — else the unchanged text
path. terminalId is the callId; cwd defaults to the session header. The pure
translator gained a TerminalRendering {enabled,cwd} param (off by default).
Tests via the REAL tool-bash + bash-local: capability ON -> terminal content +
_meta; OFF -> no _meta (text path). The with-key e2e adds a real-model terminal
card case (echo over ACP with the capability on). 773 tests, 100% coverage.
The exit-status pill (_meta.terminal_exit), live streaming
(_meta.terminal_output_delta), and command classification are RFC follow-ups.
108 lines
7.4 KiB
Markdown
108 lines
7.4 KiB
Markdown
# dsh-tools
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Tool registry and execution waterfall. Tool plugins register their schemas and executors; the agent loop executes calls through the `tools/execute` waterfall.
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## Service: `ToolRegistry` (ctx key: `tools`)
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### Public API
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- `ctx.tools.register(definition: ToolDefinition): () => void` Register a tool. Disposed with the calling fiber.
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- `ctx.tools.get(name: string): ToolDefinition | undefined`
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- `ctx.tools.schemas(): ToolSchema[]` Schemas of all registered tools (without the `execute` functions).
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- `ctx.tools.execute(exec: ToolExecution): Promise<ToolExecutionResult>` Execute one tool call through the `tools/execute` waterfall.
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### Injected services
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`SystemPrompt` — the registry automatically feeds its tool schemas into the system-prompt assembly via `ctx.systemPrompt.tools()`.
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### Events
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| Event | Mode | Purpose |
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|---|---|---|
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| `tools/execute` | waterfall | Wrap/veto tool execution (sandbox, permission, hooks, plan mode) |
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| `tools/change` | emit | A tool was registered or unregistered |
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### Key types
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- `ToolDefinition` — `ToolSchema` + `execute(args, exec): Promise<ContentBlock[]>`, plus optional `presentCall(args)` / `presentResult(args, result)` for tool-owned UI presentation (see below).
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- `ToolExecution` — one pending tool call: `{ callId, name, arguments, agent?, signal? }`.
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- `ToolExecutionResult` — outcome: `{ callId, content, isError, error? }`. On failure with a `HarnessError`, `error: { name, code }` carries the structured failure class alongside the model-facing text (the loop forwards it onto the `tool/result` session event for retry/sandbox plugins and replay).
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- `ToolCallPresentation` / `ToolResultPresentation` — provider-neutral shapes a tool returns from `presentCall` / `presentResult` to own how a UI renders ITS calls (see "Tool-owned UI presentation").
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### Extension points
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- Tool plugins call `ctx.tools.register()` — schemas flow into the assembly automatically.
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- The `tools/execute` waterfall is the single seam for sandbox, permission, hooks, and plan-mode plugins to wrap or veto a call. Listeners receive `(exec, next)`: call `next()` to proceed, or return a result without calling `next()` to short-circuit (veto).
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- MCP servers: one plugin per server, discover tools, call `ctx.tools.register()` with the server's schemas.
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### Typed tool parameter schemas
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First-party plugin authors can use the `defineTool()` helper (exported from this package) for typed tool parameter schemas:
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```ts
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import { readFile } from 'node:fs/promises'
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import type { Context } from 'cordis'
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import { defineTool } from '@deepseek-ai/dsh-tools'
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declare const ctx: Context
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ctx.tools.register(defineTool({
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name: 'read_file',
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description: 'Read a file from disk.',
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parameters: {
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path: { type: 'string', required: true, description: 'Absolute file path' },
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offset: { type: 'number' },
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limit: { type: 'number' },
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},
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async execute(args, exec) {
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// args is typed: { path: string; offset?: number; limit?: number }
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const text = await readFile(args.path, 'utf8')
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return [{ type: 'text', text }]
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},
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}))
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```
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The helper converts the author-facing `SchemaSpec` (with `required: true` as a per-property boolean) to standard JSON Schema for the wire format. Raw JSON-Schema tool definitions (from MCP servers) are still accepted by the registry directly.
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A `defineTool` tool also **validates the model-generated arguments against its `SchemaSpec` before `execute` runs** (`validateArgs`). The model's JSON is untrusted — `InferArgs<S>` is a compile-time claim, not a runtime guarantee — so on a mismatch (missing required key, wrong primitive, bad enum member, nested violation) the tool throws a `ToolArgsError` (`code: 'INVALID_ARGS'`); the registry turns it into an `isError` result whose text lists the violations, which the model sees and self-corrects from. Validation mirrors the JSON Schema conversion exactly: extra keys are allowed, `default` is not applied, and an `object`/`array` prop without `properties`/`items` only type-checks. Raw-registered tools (MCP) are **not** validated by the harness — they validate their own input.
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See `defineTool`, `validateArgs`, `ToolArgsError`, `SchemaSpec`, `InferArgs`, and `schemaSpecToJsonSchema` in the public API for details.
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### Tool-owned UI presentation
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A tool owns how ITS calls render in a UI (an editor's tool-call card, a CLI log line) — a UI plugin must NOT special-case tool names. A `ToolDefinition` may declare two optional, pure, display-only methods:
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- `presentCall(args): ToolCallPresentation | undefined` — the PENDING state: a human-readable `title` (always-visible label), an optional `kind` (`read`/`edit`/`execute`/… for icon/treatment, default `other`), an optional `rawInput` (the salient input to show in a detail view — e.g. a shell command as a string, NOT the whole args object), and an optional `terminal` (a neutral `{ cwd? }` asking a capable UI to render this call as a TERMINAL, e.g. for `bash`).
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- `presentResult(args, result): ToolResultPresentation | undefined` — the COMPLETED state, given the same `args` and the `{ content, isError }` result: an optional replacement `title`, reformatted `content` (e.g. wrap command output in a fenced ` ```console ` block — a UI-only affordance that must NOT appear in the model-facing `execute` result), and an optional `terminal` (the `{ output? }` for a terminal-rendered call). The `ToolTerminal` shape is provider-neutral; a UI bridge (the ACP bridge) maps it to a terminal card and a UI that can't ignores it and uses `content`.
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Returning `undefined` (or omitting a method) tells a UI to fall back to a generic presentation (title = tool name, raw args as input, raw result content). Both methods must be **pure and side-effect-free**: a UI may call them during live streaming AND during a session-log replay, so they depend only on their arguments. With `defineTool`, `args` is the typed `InferArgs<S>` shape; the helper soft-validates before calling (a malformed/older logged arg shape yields `undefined` rather than throwing, since display must never crash a replay). The shapes are provider-neutral — the ACP bridge (`dsh-acp`) maps them to ACP `tool_call`/`tool_call_update` wire fields, and `dsh-tool-bash` is the reference implementation.
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```ts
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import { defineTool } from '@deepseek-ai/dsh-tools'
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const bash = defineTool({
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name: 'bash',
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description: 'Run a shell command.',
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parameters: {
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command: { type: 'string', required: true, description: 'The command to run.' },
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description: { type: 'string', required: true, description: 'One-line summary shown in the UI.' },
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},
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async execute(args) {
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return [{ type: 'text', text: `ran: ${args.command}` }]
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},
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// The model-written description is the readable title; the command is the detail.
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presentCall: args => ({ title: args.description, kind: 'execute', rawInput: args.command }),
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// Wrap the output as a console block for the UI (not in the model-facing result).
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presentResult: (_args, result) => {
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const block = result.content.length === 1 ? result.content[0] : undefined
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if (block === undefined || block.type !== 'text') return undefined
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return { content: [{ type: 'text', text: '```console\n' + block.text + '\n```' }] }
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},
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})
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```
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### What is NOT here (TODO)
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- **Tool shapes review** — when real tools land (e.g. a concurrency-safety hint for parallel execution); phase 1 executes tool calls sequentially.
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- **Parallel execution** — the loop currently iterates tool calls sequentially.
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