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echo-xianyu

Danbooru Search MCP

by echo-xianyu

danbooru_search_character

Read-onlyIdempotent

Analyze a character's visual traits by retrieving the tags that most frequently appear alongside them on Danbooru. Enter a character tag to see defining features like hair color, eye color, and accessories.

Instructions

Get visual trait frequencies for a character tag.

Best tool for character visual trait analysis. Use danbooru_get_character_profile if you also need wiki/implications. Given a character or copyright tag (e.g. hoshino_(blue_archive)), it returns the tags that most frequently appear alongside it on Danbooru posts. A tag with frequency 0.92 (like ahoge or pink_hair for Hoshino) appears in 92%% of posts carrying the query tag, so it is a defining visual trait.

Results are ordered by co-occurrence frequency (descending). Meta tags such as highres are excluded by default because they describe image quality, not the character.

When auto_resolve is enabled (default) and the queried tag does not exist on Danbooru, the tool automatically queries the autocomplete endpoint to find the correct name (e.g. correcting a misspelled amamya_kokoro to amamiya_kokoro), re-runs the search with the corrected name, and reports the correction in resolved_from.

Args: params (SearchCharacterInput): Validated input: - tag (str): Canonical Danbooru tag, e.g. 'hoshino_(blue_archive)'. - limit (int): Max related tags to return (1-100, default 25). - category (Optional[TagCategory]): Filter to one category. - min_frequency (float): Min co-occurrence frequency 0-1. - exclude_meta (bool): Drop meta tags (default True). - response_format (ResponseFormat): 'markdown' or 'json'. - auto_resolve (bool): Auto-correct misspelled tags (default True).

Returns: str: Markdown table or JSON. Success JSON schema: { "query": str, "tag": {"name": str, "category": str, "category_id": int, "post_count": int}, "related_tags": [ {"name": str, "category": str, "category_id": int, "post_count": int, "frequency": float, "jaccard_similarity": float, "overlap_coefficient": float} ], "wiki_page_tags": [ {"name": str, "category": str, "category_id": int, "post_count": int} ], "resolved_from": {"original": str, "resolved": str, "corrected": bool} | null, "suggestions": [ {name, category, post_count, is_alias}, ... ] # when tag not found } Error: "Error: ".

Examples: - "What does Hoshino from Blue Archive look like?" -> params with tag='hoshino_(blue_archive)' -> returns ahoge, pink_hair, blue_eyes, heterochromia, halo, ... - "Find all copyright tags related to this character" -> params with tag='hoshino_(blue_archive)', category='copyright'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds rich behavioral context: results ordered by co-occurrence frequency, default exclusion of meta tags, auto_resolve behavior for misspelled tags, and what happens when a tag is not found. No contradictions with annotations.

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

Conciseness5/5

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

The description is well-structured with clear sections: purpose, usage comparison, auto-resolve explanation, Args, Returns, and Examples. It is front-loaded with the key information and is not overly verbose for the tool's complexity.

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?

The description covers all necessary aspects: purpose, input parameters, return format (including JSON schema), error handling, examples, and differentiation from siblings. It is complete enough for an agent to use the tool correctly without external information.

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?

The input schema already provides detailed descriptions for all parameters (high coverage). The description adds value by including examples (e.g., tag canonical form), clarifying the auto_resolve behavior, and explaining the meaning of frequency. This extra context justifies a score above the baseline of 3.

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's purpose: 'Get visual trait frequencies for a character tag.' It explicitly differentiates from the sibling tool 'danbooru_get_character_profile', saying to use that if wiki/implications are needed. The verb and resource are specific.

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 provides explicit when-to-use guidance ('Best tool for character visual trait analysis') and when-not-to-use ('Use danbooru_get_character_profile if you also need wiki/implications'). It also gives concrete examples of queries, which helps an agent select the tool correctly.

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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