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Reqbook makes agents API-capable.

Agents are great at writing code but struggle with API work — they can’t reliably run requests from inside the session, produce API specs that stay in sync with the code, or review results in a structured way. Reqbook gives agents the building blocks they need: markdown files they can read and write, tools that return structured results, and playbooks that know when to call the CLI and when to use LLM reasoning.

Skills

A single markdown playbook that teaches agents the spec format, file layout, and decision trees — installed to the directory each agent reads from automatically.

Slash commands

Two /rqb commands for the complex, LLM-native tasks: spec creation and enrichment, flow building, and debugging.

MCP server

Ten structured tools for MCP-compatible agents — exec, diagnose, flow, author, vars, search, context, history, session, and exec_batch. Surgical context by default.

Shared source

Every agent change is a markdown file in api-docs/. Reviewable, diffable, committable. No hidden state.

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The division of labor

The key design decision: not every operation needs an agent. Simple, deterministic operations belong in the terminal. Agents add value when a task requires reasoning about your codebase, your domain model, or the relationships between data.

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Skills — all agents

Skills are the “always-on” layer: installed to each agent’s config directory, they activate automatically when API-related topics come up in the conversation. No explicit command needed. One skill covers the full workflow:
  • rqb — Project layout, endpoint spec format, Assertions operators, pipeline capture patterns, MCP tool reference, and routing rules for when to use each slash command.
Installed via rqb skills install to agent-specific locations (.claude/skills/, .cursor/rules/, .github/instructions/, etc.).

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Slash commands — Claude Code and Codex CLI

Two commands covering the tasks where LLM reasoning produces better results than a fixed script:

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MCP server — MCP-compatible agents

Ten tools that return structured JSON or surgical context for the agent to act on programmatically: Spec and pipeline tools resolve variables the same way as the CLI: _shared/env.md, .env.local, RQB_* / MAD_*, session vars, then explicit tool vars.
1

Install skill and slash commands

2

Register MCP

3

Open the web preview

Keep the preview open while the agent edits specs — review and run changed files immediately in the browser.
With all three active:
  • The agent knows Reqbook conventions and makes good decisions without prompting (skill)
  • You can trigger complex workflows precisely without writing long prompts (slash commands)
  • The agent gets structured results without parsing terminal output (MCP)
For implement, review, or debug runs, ask the agent to call rqb_context first with mode: "surgical", brief: true, max_fields: 12, include: "variables,request,response,errors,rules,verify", and an explicit intent such as "implement" or "debug". Use max_fields: 6 only for a known narrow lookup. If rqb_exec fails, call rqb_diagnose before reading backend source. This keeps the loop focused on the bounded contract, literal error codes, compact business rules, likely cause, inspect targets, and verify commands.

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Supported agents

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Next steps

Set up agent support

Install the skill, slash commands, and MCP in one command.

Slash commands reference

Full details on /rqb and /rqb-debug.

MCP server reference

All 9 tools with input/output schemas and examples.