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KlausFreiberufler

DevFlow MCP Server

devflow_token_emit

After a discipline-skill completes, emit an HMAC-signed token as proof to authorize flow state transitions under agent_with_discipline policy.

Instructions

Emit a discipline-token for a flow + skill (DF-289 backend, DF-292 gate-check).

A discipline-token is an HMAC-signed proof that a discipline-skill ran successfully. The signed token is returned ONLY once — the backend stores only the hash. Keep the signed token in session memory.

Use this at the END of a discipline-skill run (devflow-tdd, devflow-verification-gate, devflow-adr-compliance, devflow-collision-acknowledged, ...) once all iron-laws of the skill are satisfied.

Pass the token later to flow_update when transitioning under agent_with_discipline policy: flow_update({ flowId, currentState: 'done', selfApproved: true, disciplineTokens: [token] })

The backend verifies all required tokens before allowing the transition. Without an opt-in project (project_configs.allow_agent_self_approval=1) and matching pipeline_steps.required_skills, the gate-check rejects.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
flowIdYesFlow id this token belongs to
skillNameYesDiscipline-skill name (e.g. "devflow-verification-gate")
evidenceNoOptional structured evidence (cycles, hashes, criteria results — kept verbatim for audit)
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses that the token is returned only once, backend stores only the hash, keep in session memory, HMAC-signed, and gate-check rejection conditions. No annotations provided, so description fully covers behavioral aspects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with clear sections: definition, behavioral note, usage instruction, downstream usage, and backend details. Every sentence adds value; no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no annotations and no output schema, the description is complete: explains token nature, usage timing, downstream integration, and prerequisites. Sufficient for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and description adds value by explaining that evidence is optional, structured for audit, and provides examples of skill names. Though schema covers the basics, description enriches context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the verb 'emit' and the resource 'discipline-token' with specific context ('for a flow + skill'), clearly distinguishing it from siblings like 'devflow_tokens_list' which lists tokens.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states to use at the END of a discipline-skill run after all iron-laws are satisfied, and explains downstream usage with flow_update. Does not explicitly state when not to use, but context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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