directive 0.0.7

Directive CLI and MCP server scaffolding for file-driven agent context.

Directive: Spec‑first approach to working with AI coding agents

Write specs, not chats.

A spec‑first approach to increase coding agent accuracy and developer efficiency. It replaces ad‑hoc back‑and‑forth with concise, versioned specs that become the canonical history of your work.

Problems this aims to solve:

• Improving agent accuracy and developer efficiency: Clear specs reduce ambiguity and rework, speed up iterations, and align expectations between humans and agents.
• Replacing chatty back‑and‑forth with upfront, versioned specs: Author concise specs first to avoid prompt drift; keep a single source of truth that onboards collaborators quickly.
• Specs as durable, reviewable artifacts and canonical history: Spec → Impact → TDR live in the repo, capturing decisions and enabling traceability; Spec→Test mapping turns requirements into verification.

How it works (brief): Work is gated by explicit review checkpoints — Spec → Impact → TDR — with no code before approval. After approval, follow strict TDD with Spec→Test mapping. Everything lives in‑repo and is exposed via a tiny MCP server for IDEs like Cursor. See the supporting background in Research & Rationale.

Quickstart (CLI + MCP)

• Install (using uv):
  • In a project: uv add directive (adds to pyproject.toml and uv.lock)
• Initialize defaults in your repo:
  • uv run directive init (non-destructive; creates directive/ with AOP, Context, and templates)
• Configure your MCP-aware IDE/agent to launch the server:
  • Command: uv run directive mcp serve (stdio)
  • Tools are auto-discovered via tools/list; the agent will fetch Spec/Impact/TDR templates and context automatically.
• (Optional) Inspect a bundle directly:
  • uv run directive bundle spec_template.md (prints a JSON bundle to stdout)

Exposed tools (discovered automatically)

• directive/templates.spec: Spec bundle (AOP, Agent Context, Spec template, Primer)
• directive/templates.impact: Impact bundle
• directive/templates.tdr: TDR bundle
• directive/files.get: Read a file under directive/ by path
• directive/files.list: List files under directive/

Using with Cursor (MCP)

1. Ensure your project has Directive installed and initialized:
   • uv add directive
   • uv run directive init
2. MCP config for Cursor (auto‑created by directive init if missing):
   • uv run directive init will create .cursor/mcp.json and .cursor/servers/directive.sh if they don't exist.
   • If you already have .cursor/mcp.json, copy/merge the following JSON into your existing file:

{
  "mcpServers": {
    "Directive": {
      "type": "stdio",
      "command": "/usr/bin/env",
      "args": ["-S", "uv", "run", "-q", "-m", "directive.cli", "mcp", "serve"],
      "transport": "stdio"
    }
  }
}

3. You should now be able to see the enabled MCP server in Cursor Settings -> MCP

Workflow

The Agent Operating Procedure (/directive/reference/agent_operating_procedure.md) is a concise, enforceable checklist that defines the Spec → Impact → TDR → Implementation flow and its review gates.

To use it in your project, you can use the MCP server (auto-discovers templates and context), or do it manually by including the single directory /directive/reference/ in the agent's context (contains agent_operating_procedure.md, agent_context.md, and templates).

Step 1 — Customize Agent Context

• Tailor /directive/reference/agent_context.md to your project (languages, tooling, conventions, security, testing). Refer to agent_operating_procedure.md for the end‑to‑end flow.

Step 2 — Spec (behavior/UX‑only)

• Define desired behavior, interfaces, user outcomes, and clear acceptance criteria. Save as /directive/specs/<feature>/spec.md (template: /directive/reference/templates/spec_template.md).

Step 3 — Impact Analysis (approve before TDR)

• Identify modules/packages touched, contract changes (APIs/events/schemas/migrations), risks, and observability needs. Save as /directive/specs/<feature>/impact.md (template: /directive/reference/templates/impact_template.md).

Step 4 — Technical Design Review (TDR) (approve before coding)

• Decide interfaces and behavior. Include a brief Codebase Map, data contracts, error handling, observability, rollout, and Spec→Test mapping. Save as /directive/specs/<feature>/tdr.md (template: /directive/reference/templates/tdr_template.md).

Step 5 — Start implementation (after TDR approval)

• Begin coding guided by the TDR and your agent_context.md. Use tests to validate behavior and keep CI green.

Gates: Spec → Impact → TDR → Implementation (no code before TDR approval).

Research & Rationale

This framework is grounded in current best practices for spec‑driven development with AI coding agents. Below is a distilled summary of the sources we align to and the principles that inform the workflow.

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Key Practices from the Field

1. Make the Spec the Source of Truth

• Specs live in the repo, not in ephemeral chats.
• They drive planning, tasks, and validation.
• GitHub’s Spec Kit formalizes this into a 4-phase loop: Specify → Plan → Tasks → Implement.
• Specs aren’t static — they are executable artifacts that evolve with the codebase.
  🔗 Spec Kit (GitHub Blog)

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2. Separate the Stable “What” from the Flexible “How”

• Capture what the system must do in product terms (user outcomes, interfaces, acceptance criteria).
• Keep how it is built flexible and expressed later in technical design docs.
• Example: Kiro’s approach outputs requirements.md, design.md, and tasks.md separately.
  🔗 Kiro: Spec-First Development

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3. Tie Every Requirement to a Test (“Executable Specs”)

• Every spec clause must map to a test, often written in Given–When–Then (BDD style).
• Track spec coverage (all spec items tested) in addition to code coverage.
• This ensures agents are judged against explicit requirements, not guesses.
  🔗 Executable Specifications & BDD (Cucumber)

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4. Use the Agent to Draft the Spec, Humans to Edit

• Approaches like “Vibe Specs” let the LLM propose the first draft through Q&A.
• Humans then critique, clarify, and cut scope creep.
• The refined spec becomes the north star for implementation.
  🔗 Vibe Spec Method

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5. Practice “Context Engineering,” Not Just Prompting

• Agents perform better when given durable, file-based context packs:
  • Rules/conventions
  • Example code patterns
  • Data contracts and schemas
  • Documentation links
• Repos that include a global rules file plus examples see much higher fidelity.
  🔗 Context Engineering (GitHub Copilot best practices)

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6. Choose Method by Risk/Complexity; Enforce Verification

• For low-risk features: lightweight specs may suffice.
• For high-risk or complex builds: follow Spec-Then-Code, with rigorous review gates.
• Use multi-AI cross-review or human checkpoints where the blast radius is large.
  🔗 Spec-Then-Code Methodology

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7. Industry is Moving Toward Templates

• Beyond open-source tools, groups like TM Forum have published formal AI Agent Specification Templates for enterprise contexts.
• Standardization is arriving, which signals the importance of shared spec formats.
  🔗 TM Forum AI Agent Specification Template

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8. A Pragmatic Solo/Dev Flow Works Today

• A repeatable loop many developers use:
  1. Brainstorm a spec
  2. Generate a step-by-step plan
  3. Execute with a codegen agent in small, testable chunks
  4. Keep artifacts checked into the repo (spec.md, prompt_plan.md, todo.md).
     🔗 Solo Dev Spec Loop (Indie Hackers)

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Takeaway

• Specs must be concise, testable, and versioned.
• AI agents thrive when specs are paired with context packs and a TDD-first workflow.
• The winning approach is not over-specifying implementation, but rigorously specifying outcomes, contracts, and tests.