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papjuli

xkcdai

by papjuli

find_xkcd

Finds semantically relevant xkcd comics for a given context. Returns results with similarity scores to help select strong conversational matches.

Instructions

Find xkcd comics semantically relevant to the current conversation.

Call this whenever an xkcd comic might enrich the conversation — when the discussion lands on a topic xkcd is famous for skewering (programming, science, statistics, relationships, the absurdity of standards, etc.).

Pass a concise description of the current topic or theme as context (a phrase or sentence works better than a whole transcript), e.g. "spending hours automating a task that was faster to do by hand" or "code finally compiling".

IMPORTANT — deciding whether to mention one. xkcd has a comic for almost every topic, so this tool will nearly always return something. A result being returned does NOT mean you should bring it up. Use the score as a signal and apply your own judgment about conversational fit: score >= 0.75 strong match — usually worth mentioning if it fits the moment 0.66 - 0.75 plausible — mention only if it genuinely lands < 0.66 weak/tangential — almost always better to stay silent Only one comic, at most, per topic — and only when it actually adds something. When in doubt, say nothing; a forced reference is worse than none.

When you do share one, cite it by number and title with its url, and quote the alt (mouseover) text — it's half the joke.

Returns a dict with a results list (num, title, score, url, image, alt, explain_url) and a count. An empty list means nothing cleared the floor.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contextYes
min_scoreNo
max_resultsNo
Behavior5/5

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

No annotations are provided, so the description carries full burden. It discloses that the tool nearly always returns something, explains the return format (dict with results list and count), and describes the score field and citation conventions. This fully exposes behavioral traits.

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 lengthy but well-structured, with purpose first, then usage, caution, and output details. Each sentence adds value; however, some redundancy could be trimmed. It remains clear and organized.

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 fully explains return values and fields. It covers when to call, how to use parameters, interpretation of results, and how to cite. It is complete for the tool's moderate complexity.

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 0%, but the description adds significant meaning for the 'context' parameter with examples and guidance. For 'min_score', it provides score thresholds that relate to the default, but 'max_results' is not explicitly explained. Overall, the description compensates well for the lack of schema descriptions, though not exhaustively.

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 xkcd comics semantically relevant to the conversation. The verb 'find' and resource 'xkcd comics' are specific, and the context of 'current conversation' is unambiguous. No sibling tools exist, so differentiation is not required.

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: 'Call this whenever an xkcd comic might enrich the conversation — when the discussion lands on a topic xkcd is famous for skewering.' It also includes when-not-to-use: 'A result being returned does NOT mean you should bring it up,' and gives scoring thresholds for decision-making.

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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