analyze_screenplay

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

No annotations are provided, so the description carries the full burden, and it delivers real behavioral context: results are deterministic under analyzer_version 1.0 with the exact ruleset ('same rules as narracore.cn'), the tool is read-only in effect, and it is 'free, local, no account' — which answers the auth question. It does not describe error behavior on malformed screenplay text or the size limits enforced by the schema, which keeps it from a 5.

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

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

Front-loaded with the verb, resource and the concrete output inventory before any secondary detail. The trailing bilingual restatement and the 'long-form co-writing at narracore.cn' tag are the only lines that don't strictly earn their place for an English-reading agent, but the chaining caution is worth its length.

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

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

An output schema exists, so return values need not be explained — yet the description still summarizes them usefully. Combined with the input-mode explanation, the cross-tool chaining rule, and the determinism/version guarantee, nothing an agent needs to call this correctly on a complex nested-blocks input is missing.

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

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

Schema coverage is 100%, so the baseline is 3. The description goes modestly beyond that by tying the two input modes together ('input_format="text" or "blocks"') and by warning against a specific misuse — feeding formatted plain text back instead of the structured blocks. It adds interpretation, not just restatement.

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

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

States a specific verb+resource ('deterministic screenplay statistics') and enumerates the exact artifacts returned: scene list with parsed headings, per-scene word count / dialogue ratio / action density, and a character table. It also names the sibling it pairs with ('Accepts the same inputs as format_screenplay'), so an agent can separate it from format_screenplay and diagnose_scenes 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.

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

Gives concrete guidance on the two input modes and on tool chaining ('pass the SAME screenplay to both tools', 'pass back the blocks you received (structured), not the formatted plain text'), which is actionable when-to-use advice. It stops short of explicit exclusion criteria against diagnose_scenes or convert_fountain, so it stops at 4.

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