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NalluriTanavreddy

slugline-mcp

find_mood_reference_scenes

Find reference scenes that achieve a target mood by entering any mood description, using semantic search when exact tag isn't available.

Instructions

Find reference scenes that strongly achieve a target mood.

Returns: A dict with: - method: "tag_matched" if target_mood closely matched a precoded mood tag, "semantic_fallback" if it didn't and results came from raw nearest-neighbor search instead, or "unavailable" if the reference index isn't available. - matched_tag / tag_similarity: the closest precoded tag and its cosine similarity to target_mood, regardless of which method was used -- lets the caller judge match confidence even on the fallback path. - results: the ranked scene matches themselves. Empty if no scenes are available or the index is unavailable.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
top_kNoMaximum number of matches to return.
target_moodYesThe mood to rewrite toward, in free text (e.g. "paranoid", "wistful", "a creeping sense of being watched"). Doesn't need to match a precoded label -- close matches use a precise tag-filtered search, anything else falls back to raw semantic search so results are still returned.
Behavior5/5

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

Despite no annotations, the description fully discloses behavioral traits: two search methods, conditions for each, return dict keys, and behavior when index is unavailable.

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 well-structured with a clear lead sentence and bulleted return section. It is thorough but could be slightly more concise.

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?

Given no output schema, the description thoroughly documents return format and all scenarios. The tool's behavior is fully specified.

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 100%, but the description adds value by explaining how target_mood works (free text, fallback behavior) and clarifying top_k as maximum matches.

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 clearly states the tool finds reference scenes for a target mood, distinguishes two methods based on input closeness, and explains the return structure.

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 description explains when each internal method is used (tag_matched vs semantic_fallback) and notes that target_mood can be free text. However, no sibling tools are listed, so no direct comparison with alternatives.

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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