Narracore Format

diagnose_scenes

Validate and score screenplay scene shootability: fetch numbered scene material, submit findings with verbatim evidence, and receive per-scene 0-100 scores with verified coverage.

Instructions

Scene-level "shootability" diagnosis for a screenplay, using the same ai-2.0 rules as narracore.cn: YOU (the calling AI) cite the evidence, the tool validates it and computes the score — no LLM inside. Two steps: (1) mode="material" returns each scene as a numbered block list ([n] [type] text), the strict findings JSON schema, and 6 problem types (abstract_psychology, literary_summary, unfilmable_information, unclear_space, weak_visible_action, unplayable_direction); (2) after analyzing the material yourself, call mode="score" with findings=[{scene, summary, problems:[{type, severity(low|medium|high), evidence (verbatim quote from the material), blocks (ordinals), suggestion}]}]. The tool maps ordinals to blocks, drops any finding whose evidence cannot be verified (that scene then scores null — never faked), and computes each scene's 0-100 score (severity-weighted evidence coverage, exact arithmetic) plus a reference-only structural baseline. 剧本逐场影视化度诊断：AI 举证、程序算分（0-100，证据核验，无效即空不冒充）。Part of the Narracore toolkit — free, local, no account; long-form co-writing at narracore.cn.

Input Schema

TableJSON Schema

Name	Required	Description
`mode`	No	Required, one of material \| score: material returns the numbered scene material + schema; score submits findings for validation and scoring.
`text`	No
`blocks`	No
`scenes`	No	material mode only: scene numbers (1-based) to render; default all
`findings`	No	score mode only: your per-scene findings
`request_id`	No	Trace id (optional)
`input_format`	No	text (default): line-based screenplay text. blocks: typed blocks (e.g. from format_screenplay).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`ok`	Yes
`mode`	No
`tool`	No
`reason`	No
`scenes`	No
`schema`	No
`message`	No
`overall`	No
`recovery`	No
`versions`	No
`next_step`	No
`request_id`	No
`diagnostics`	No
`problem_types`	No
`contract_version`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 19, 2026

TDQS

A4.3/5.0

Behavior5/5

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

With no annotations, the description carries the full load and does so well: it states no LLM runs inside, that the tool maps ordinals to blocks, drops findings whose evidence cannot be verbatim-verified, and assigns the scene a null score rather than faking one, plus exact severity-weighted arithmetic for the 0-100 score. These are meaningful behavioral traits an agent cannot infer from the schema.

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?

Front-loaded with purpose and clearly segmented into labeled steps (1) and (2), so an agent can parse it quickly. It loses points for a full Chinese sentence that restates the English mechanism verbatim and a trailing promotional line about the Narracore toolkit and narracore.cn, neither of which helps invocation.

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?

For a two-step, evidence-validating scorer this is complete: both modes, the input contract, the verification rule, the null-score fallback and the scoring method are all covered, and an output schema exists so return values need not be described. An agent has everything it needs to run the protocol correctly.

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 71%, so the schema already documents most parameters, but the description adds real semantics: the two mode values and their sequence, the required shape of a finding, that 'evidence' must be a verbatim quote, and that 'blocks' are ordinals referring to the numbered material output. That linkage between the two modes is not obtainable from the schema alone.

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 verb and resource ('Scene-level shootability diagnosis for a screenplay') and enumerates the exact scope: 6 named problem types and a 0-100 per-scene score. The two-mode workflow is spelled out concretely. It does not, however, explicitly distinguish itself from the sibling 'analyze_screenplay', which is the likeliest source of agent confusion.

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?

The ordering is explicit and actionable: call mode='material' first to get numbered blocks and schema, then 'after analyzing the material yourself' call mode='score' with findings. That sequencing is unusually clear. It stops short of saying when to prefer this tool over analyze_screenplay or when not to use it at all.

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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