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Tool Calling — 技能工具

v1.0.0

[自动翻译] Deep workflow for LLM tool/function calling—schema design, validation, permissions, errors, idempotency, testing, and safe orchestration with agents. ...

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by @mikeclaw007·MIT-0
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License
MIT-0
最后更新
2026/3/25
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OpenClaw
安全
high confidence
This is an instruction-only guidance skill about designing and operating LLM tool/function calling; its content, scope, and lack of installs or credentials are coherent with its stated purpose.
评估建议
This skill is a safety-and-design playbook, not executable code—so installing it has minimal direct risk. Before you rely on it in production: (1) ensure any real tool implementations enforce server-side schema validation, authorization, and output filtering as the guide recommends (do not trust the model); (2) avoid giving the model direct filesystem, database, or credential access—use validated server-side adapters and least-privilege principals; (3) if you will allow autonomous agent invocati...
详细分析 ▾
用途与能力
Name and description match the SKILL.md: the document is guidance for designing tool schemas, validation, authz, idempotency, errors, and testing. There are no binaries, env vars, or installs requested that would be unrelated to the stated purpose.
指令范围
SKILL.md contains best-practice guidance and checklists only; it does not instruct the agent to read arbitrary files, access environment variables, download or transmit data to external endpoints, or perform system actions. It warns about risky patterns (raw SQL, shell, filesystem) rather than instructing their use.
安装机制
No install spec and no code files — instruction-only. This minimizes on-disk footprint and execution risk.
凭证需求
The skill declares no required environment variables, credentials, or config paths. The SKILL.md discusses carrying user-scoped credentials as a design topic but does not request any secrets or access itself.
持久化与权限
Flags are default (always:false, model invocation allowed). The skill does not request permanent presence or to modify other skills or system settings.
安全有层次,运行前请审查代码。

License

MIT-0

可自由使用、修改和再分发,无需署名。

运行时依赖

无特殊依赖

版本

latestv1.0.02026/3/24

- Initial release with a comprehensive workflow for safe, reliable tool/function calling with LLMs. - Covers contract design, schema validation, permission checks, error handling, idempotency, and orchestration best practices. - Outlines a six-stage process: tool surface definition, schema & validation, authorization & safety, execution semantics, errors & observability, and evaluation & regression. - Provides actionable principles, anti-patterns, and exit conditions for each stage. - Includes a final review checklist and practical tips for ensuring secure and predictable tool integrations.

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安装命令 点击复制

官方npx clawhub@latest install tool-calling
镜像加速npx clawhub@latest install tool-calling --registry https://cn.clawhub-mirror.com

技能文档

Tool calling is contract design between a probabilistic planner (the model) and deterministic systems. Failures are usually schema, permissions, or ambiguity—not the LLM “being dumb.”

When to Offer This Workflow

Trigger conditions:

  • Designing OpenAI/Anthropic-style functions, MCP tools, or internal JSON tool protocols
  • Debugging wrong arguments, hallucinated parameters, or unsafe side effects
  • Building agents with many tools—selection and routing problems

Initial offer:

Use six stages: (1) define tool surface, (2) schema & validation, (3) authz & safety, (4) execution semantics, (5) errors & observability, (6) evaluation & regression. Confirm side-effect class (read-only vs write).


Stage 1: Define Tool Surface

Goal: Minimize tools; maximize clarity per tool.

Principles

  • One action per tool when possible—avoid mega-tools with mode flags unless necessary
  • Names descriptive: search_orders not do_stuff
  • Prefer idempotent operations where writes exist; separate read vs write clearly

Anti-patterns

  • Exposing raw SQL or shell to the model
  • Too many overlapping tools → routing errors

Exit condition: Tool list with purpose, inputs, outputs, side effects table.


Stage 2: Schema & Validation

Goal: Arguments are typed, constrained, and machine-validated before execution.

Practices

  • JSON Schema: enums, min/max, patterns, required fields
  • Normalize dates, IDs, currencies server-side—never trust model formatting alone
  • Default behaviors explicit in description + schema

Descriptions

  • Tool and parameter docstrings seen by model—precise language; examples of valid args

Exit condition: Validator rejects invalid args with actionable errors back to model or orchestrator.


Stage 3: Authorization & Safety

Goal: Every tool call runs as some principal with least privilege.

Patterns

  • User-scoped credentials carried from session; tool implementation re-checks ownership (e.g., order_id belongs to user)
  • Admin tools behind explicit allowlists and human approval when needed
  • Rate limits per user + global circuit breakers

Data exfiltration

  • Tools that read sensitive data need output filtering and logging policies

Exit condition: Threat brief: “What if model is tricked into calling tool X?” answered.


Stage 4: Execution Semantics

Goal: Clear transactionality, retries, and idempotency.

Design

  • Idempotency keys for writes; dedupe window
  • Timeouts and cancellation propagation
  • Ordering: parallel safe vs must be serial

Long operations

  • Async jobs with poll tool vs blocking calls—prefer non-blocking for UX and cost

Exit condition: Semantics documented for retry behavior (at-least-once delivery common).


Stage 5: Errors & Observability

Goal: Model (or orchestrator) can recover from failures without leaking internals.

Error messages

  • Structured error codes: ORDER_NOT_FOUND, PERMISSION_DENIED
  • Hints for model on how to fix—without stack traces to end users

Observability

  • Trace IDs across tool calls; audit log for write tools (who/when/args hash)

Exit condition: Dashboards/alerts on tool error rate, latency, denials.


Stage 6: Evaluation & Regression

Goal: Tool changes are tested like APIs.

Harness

  • Golden conversations with expected tool calls (args normalized)
  • Adversarial prompts attempting privilege escalation
  • Version tools; deprecate with compatibility window

Exit condition: CI or manual eval suite before deploying new tools/schemas.


Final Review Checklist

  • [ ] Minimal orthogonal tool set
  • [ ] Strict schema validation on server
  • [ ] AuthZ enforced per call; sensitive reads controlled
  • [ ] Idempotency and timeouts defined for writes
  • [ ] Structured errors + observability + eval harness

Tips for Effective Guidance

  • Treat tool descriptions as API docs the model reads—iterate wording like UX copy.
  • Recommend two-step patterns for dangerous ops: propose → confirm (human or policy).
  • When using MCP, same discipline—server must validate everything.

Handling Deviations

  • Read-only RAG: fewer semantic risks—still validate query args and injection into search backends.
  • Local tools (filesystem): sandbox, path allowlists, size limits.
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