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Glama

get_script_format_spec

Retrieve the platform-approved script format contract: markdown template, external prompt, and filled example, so external AI rewrites comply with production annotations and pass validation.

Instructions

取本平台认可的剧本格式契约:markdown 范本全文 + external_prompt(可整段转发给任意外部 AI 的任务提示词) + skeleton(带 〈…〉 占位的模板) + filled_example(填好的成品对照)。免费·静态·不扣费。★范本 v2 起把制作层标注全写进去了([角色档案] 六段格式/[外貌] 行首标记/[道具]/[SFX][BGM][VFX]/motif/场景头第四段光线/文末元信息)——v1 只写剧情层,外部 AI 因此产不出完整稿、反复过不了自检。转发时务必把 filled_example 一起给:LLM 的格式正确率靠可模仿的完整样例,不靠规则条文。★什么时候用:客户想自己掌控改写幅度、要拿到别的 AI 平台去改写、或反馈"你们的 AI 把我的剧本改动太大"时。★两条出口(先问客户要哪条,这决定外部 AI 要不要写标注):【A 直接采用】外部 AI 产出含制作层标注的完整稿 → check_script_format 全绿 → adopt_external_script 直接落为可拍稿,我方 AI 完全不介入、秒级、不计费,客户写的 [角色档案] 原样生效。适合已经把外部 AI 调顺、要求零改动的客户。【B 送 AI 改写】外部 AI 只产出剧情层(不要写任何标注) → set_script 灌回原稿位 → rewrite_script 走保真两步补标注。★B 路径下客户自己写的标注会被 G1 剥掉重写,等于白写——所以选 B 就要明确告诉外部 AI 别写标注。★怎么用(三步):① 本工具取 external_prompt + filled_example + markdown,连同客户原稿一起交给那个平台;② 拿回整理稿先用 check_script_format 自查,errors 清零;③ 按出口 A 调 adopt_external_script,或按出口 B 调 set_script + rewrite_script。★契约内容含:场景头三段格式/地点命名律/环境首句三要素/角色首次出场外貌行/声音行前缀/单行台词长度/禁写运镜与片尾标记,以及"台词逐句保留·人物不许丢·动作节拍不许丢·不许加戏"四条保真要求(已写进 external_prompt)。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
drama_titleNo仅用于范本抬头,不影响格式契约本身
episode_numberNo仅用于范本抬头

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.57

TDQS

A4.6/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full transparency burden. It discloses that the tool is free/static/no-charge, explains the v1 vs v2 content difference, warns that filled_example must be forwarded for LLM compliance, and reveals the B-path behavior where client annotations get stripped by G1. This is unusually candid about behavioral traits and downstream consequences.

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 long, but it is well-structured with ★ delimited sections and front-loads the core deliverable list. Every section earns its place by carrying actionable workflow or failure-prevention information. A little trimming of the v1/v2 background would improve conciseness, but the density is justified for a tool embedded in a multi-step pipeline.

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 no output schema and no annotations, the description fully compensates by covering return contents, when to use it, how to use it in three steps, which downstream tools to call for each exit path, and even the fidelity/format rules shipped inside the spec. An agent has all necessary information to select and invoke the tool correctly.

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%: both drama_title and episode_number already state they only affect the template header and not the contract itself. The description does not add parameter-specific details, but none are needed, so the baseline 3 applies.

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 opens with a specific verb+resource: '取本平台认可的剧本格式契约' (get the platform's recognized script format contract), and immediately enumerates the four deliverables (markdown, external_prompt, skeleton, filled_example). This distinguishes it from siblings like get_script (retrieve an actual script) and check_script_format (validate format), leaving no ambiguity about what this tool returns.

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

Usage Guidelines5/5

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

The description includes an explicit '什么时候用' (when to use) section listing three triggering scenarios, plus a three-step usage workflow referencing exact sibling tools (check_script_format, adopt_external_script, set_script, rewrite_script). It also contrasts the two exit paths A and B, telling the agent how to route based on client preference, which is far above the typical 'use this for X' guidance.

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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