deepseek-harness/packages/tools

dsh-tools

Tool registry and execution waterfall. Tool plugins register their schemas and executors; the agent loop executes calls through the tools/execute waterfall.

Service: ToolRegistry (ctx key: tools)

Public API

• ctx.tools.register(definition: ToolDefinition): () => void Register a tool. Disposed with the calling fiber.
• ctx.tools.get(name: string): ToolDefinition | undefined
• ctx.tools.schemas(): ToolSchema[] Schemas of all registered tools (without the execute functions).
• ctx.tools.execute(exec: ToolExecution): Promise<ToolExecutionResult> Execute one tool call through the tools/execute waterfall.

Injected services

SystemPrompt — the registry automatically feeds its tool schemas into the system-prompt assembly via ctx.systemPrompt.tools().

Events

Event	Mode	Purpose
tools/execute	waterfall	Wrap/veto tool execution (sandbox, permission, hooks, plan mode)
tools/change	emit	A tool was registered or unregistered

Key types

• ToolDefinition — ToolSchema + execute(args, exec): Promise<ContentBlock[]>.
• ToolExecution — one pending tool call: { callId, name, arguments, agent?, signal? }.
• ToolExecutionResult — outcome: { callId, content, isError }.

Extension points

• Tool plugins call ctx.tools.register() — schemas flow into the assembly automatically.
• 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).
• MCP servers: one plugin per server, discover tools, call ctx.tools.register() with the server's schemas.

Typed tool parameter schemas

First-party plugin authors can use the defineTool() helper (exported from this package) for typed tool parameter schemas:

import { readFile } from 'node:fs/promises'
import type { Context } from 'cordis'
import { defineTool } from '@deepseek-ai/dsh-tools'

declare const ctx: Context

ctx.tools.register(defineTool({
  name: 'read_file',
  description: 'Read a file from disk.',
  parameters: {
    path: { type: 'string', required: true, description: 'Absolute file path' },
    offset: { type: 'number' },
    limit: { type: 'number' },
  },
  async execute(args, exec) {
    // args is typed: { path: string; offset?: number; limit?: number }
    const text = await readFile(args.path, 'utf8')
    return [{ type: 'text', text }]
  },
}))

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.

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.

See defineTool, validateArgs, ToolArgsError, SchemaSpec, InferArgs, and schemaSpecToJsonSchema in the public API for details.

What is NOT here (TODO)

• Tool shapes review — when real tools land (e.g. a concurrency-safety hint for parallel execution); phase 1 executes tool calls sequentially.
• Parallel execution — the loop currently iterates tool calls sequentially.