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Dcce

mcp_m7_dcce

Generate, validate, render, and lint dual-channel content with a human-readable summary and structured AI JSON, so machine views stay structured instead of mimicking human text.

Instructions

DCCE (Dual-Channel Content Envelope) — generate, validate, render and lint dual-channel content. Two channels: Human View (3-5 sentences for humans) and AI View (structured JSON for machines). AI stays AI, no mimicry.

Input Schema

NameRequiredDescriptionDefault
aiNo
textNoText to extract DCCE from or check for DCCE presence
actorNo
humanNoHuman View text (3-5 sentences, casual tone)
actionYes
formatNo
policyNoPolicy preset name (default: generic-oss)
envelopeNo
tool_nameNoTool that generated the content (e.g. "M7 MCP")
model_nameNoModel used (e.g. "claude-opus-4-6")
content_typeNo
show_ai_viewNoInclude AI View in render (default: true)
show_provenanceNoInclude provenance info (default: false)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv3.18.1

TDQS

C2.7/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It explains the two-channel concept but does not disclose side effects (e.g., whether generate writes anything), permission requirements, validation behavior, output structure, or how policy presets affect results.

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

Conciseness4/5

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

Two sentences, front-loaded with the acronym expansion and core operations, then the channel distinction. It is appropriately sized and has little waste, though the first sentence is somewhat list-like.

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

Completeness2/5

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

This is a complex 13-parameter multi-action tool with no annotations, no output schema, and only 54% schema description coverage. The description does not explain action-specific requirements (e.g., what validate needs versus generate), policy presets, or output format, so an agent would still need to infer a lot.

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

Parameters2/5

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

Schema description coverage is only 54%, so the description should compensate. It adds some meaning for the human and AI channels, but does not explain the action enum's extract/check/policies actions, the policy preset parameter, format, content_type, envelope, or ai object semantics.

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

Purpose4/5

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

The description states a specific resource (Dual-Channel Content Envelope) and names generate, validate, render and lint as core operations, so the agent knows this tool produces and checks dual-channel content. However, it omits three valid actions from the schema enum (extract, check, policies), so its scope statement is incomplete.

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

Usage Guidelines2/5

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

The description explains what DCCE is but gives no guidance on when to use this tool versus alternatives, no exclusions, and no action-selection guidance. The sibling list contains many unrelated M7 tools, and the description does not help route between them.

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