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Read doc

read_doc
Read-only

Read a doc, presentation or storyboard as it is now, including edits the team made in the app. A doc comes back as Markdown and a presentation as its JSON. A storyboard comes back as its script: treatment, cast, each film's shots with seconds, action and lines, and a link to each picked frame. An evidence-card-ref fence is an exhibit the doc shows: keep it as it is when you rewrite the text.

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": {
      "doc_id": "<doc_id>"
    },
    "name": "read_doc"
  }
}

Input Schema

NameRequiredDescriptionDefault
doc_idYesA doc id from get_task or list_space.
promptsNoA storyboard only: also return each shot's frame prompt.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds genuinely useful behavior beyond that: the tool reflects live in-app edits, and the return shape varies by document kind (Markdown for docs, JSON for presentations, a structured script for storyboards).

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?

Four sentences, front-loaded with the core read action and then the per-format return shapes. The closing evidence-card-ref instruction is a slight detour from 'what this tool does' but is short and actionable, and there is no filler.

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?

With only two parameters and no output schema, the description carries the return-value burden and does so by enumerating what each document type yields, including the storyboard script fields. That is enough for an agent to call the tool and interpret results without further documentation.

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

Parameters3/5

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

Schema description coverage is 100%, with doc_id's provenance ('from get_task or list_space') and the storyboard-only nature of prompts already documented in the schema. The description mentions storyboard script contents but never explains the prompts flag beyond what the schema states, so it adds no meaning over structured data.

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 gives a specific verb (read) and resource (doc, presentation, storyboard) and further states the exact representation returned for each type. It stops short of contrasting itself with nearby readers such as read_roll or list_elements, so an agent must infer that distinction from the name alone.

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

Usage Guidelines3/5

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

Usage is only implied: 'as it is now, including edits the team made in the app' suggests this is the way to fetch current content rather than a snapshot from get_task, and the evidence-card-ref note implies a rewrite follow-up. There is no explicit when-to-use, when-not-to-use, or named alternative.

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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