Skip to main content
Glama
ppt-generator
by haandol

ingest_design_doc_draft

Saves the project's DESIGN.md from client draft JSON after preparation, converting theme, tone, and page requests into the design document.

Instructions

Ingests the client-generated DESIGN.md draft and saves DESIGN.md.

Call AFTER prepare_design_doc_draft with the draft JSON you generated.

Args: project_id: Project ID (required). draft_json: Draft JSON (theme + tone + anti_slop + page_requests) from the client. color_theme: Color theme (stored into the theme).

Returns: JSON with project_id and design_doc_path.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "project_id": "<project_id>",
      "draft_json": "<draft_json>"
    },
    "name": "ingest_design_doc_draft"
  }
}

Input Schema

NameRequiredDescriptionDefault
draft_jsonYes
project_idYes
color_themeNodark

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.7.0

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description must carry the full behavioral burden. It does disclose that the tool persists a file ('saves DESIGN.md') and describes the return payload, but it does not state whether an existing DESIGN.md is overwritten, whether the operation is idempotent, or what validation failures occur.

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?

The description is front-loaded with the core action and sequencing note, followed by a compact Args/Returns block. It is efficient overall, though the Returns section is redundant given the output schema.

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

Completeness3/5

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

Because an output schema exists, the explicit Returns section is unnecessary but harmless. The bigger gap is behavioral completeness for a mutation tool with no annotations and 0% schema coverage: it should mention overwrite behavior, validation expectations, or failure modes to be fully sufficient.

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 0%, so the description is doing important work: it identifies all three parameters and adds meaning beyond the schema, especially the contents of draft_json (theme + tone + anti_slop + page_requests) and that color_theme is stored into the theme. It stops short of documenting accepted color_theme values or the exact expected JSON string format.

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?

States a specific verb and resource: ingesting the client-generated draft and saving DESIGN.md. It also explicitly names the sibling step prepare_design_doc_draft, so an agent can distinguish the two without opening either schema.

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?

Gives clear sequencing: 'Call AFTER prepare_design_doc_draft with the draft JSON you generated.' That tells the agent exactly when to use it relative to an alternative. No when-not-to-use guidance or failure conditions, so it falls short of a 5.

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