Skip to main content
Glama
jhauga
by jhauga

Create tab card

create_tab_card

Render an interactive tabbed card to compare contexts of one subject, such as programming languages, OS, or skill levels. Each tab shows plain text, HTML, or code with a copy button.

Instructions

Render an interactive card with tabs, each showing a different context of the same subject (for example one tab per programming language, OS, or skill level). Tab content can be plain text, HTML, or code with a copy button.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tabsYes
titleYes
subtitleNo
tutorTermsNoEducational tutor terms. The first occurrence of each term in the card's visible prose is underlined; hovering ~1.2s shows the tip and notifies the host so the model can follow up. Terms are matched once per card, longest first, and never inside code samples, tooltips, or another term's tip. Matching is CASE-SENSITIVE, so "PATH" does not attach its tip to a filesystem "path"; set caseInsensitive on a term to match any casing. Term and tip are PLAIN text.
contextActionsNoRight-click menu actions the model anticipates being useful. Choosing one sends its prompt to the conversation; use {{selection}} to include the user's selected text.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
cardYes
partsYesSplit state, present on EVERY card result so one completeness check works for all card types. Cards that cannot split always report {current:1,total:1,hasMore:false}; create_markdown_card and create_code_tour_card pack oversized content into parts and can report more. Read hasMore rather than the card title to decide whether content was withheld.
Behavior3/5

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

With no annotations provided, the description carries the behavioral transparency burden. It discloses that the card is interactive, tabbed, and supports plain text, HTML, and code with a copy button. It does not mention behaviors like HTML sanitization or tutor-term hover interactions, though those are already documented in the schema fields, so the gap is moderate rather than severe.

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 a single well-structured sentence that front-loads the core operation, then provides concrete examples and content-type options. Every phrase earns its place and there is no filler or repetition of schema details.

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

Completeness4/5

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

The tool's core purpose is clearly explained, and the schema richly documents the optional tutorTerms and contextActions behaviors, while an output schema is present. The main missing piece is explicit guidance on when to prefer this over sibling card tools, but for a card-rendering tool with modest complexity this is adequately complete.

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

Parameters3/5

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

Schema description coverage is only 40%, so the description should compensate for undocumented parameters. It adds useful semantics for the core 'tabs' parameter: tabs are different contexts of the same subject and can hold text, HTML, or code. However, it does not add meaning for title, subtitle, tutorTerms, or contextActions beyond what the schema already documents, so compensation is partial.

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 uses a specific verb ('Render') and resource ('interactive card with tabs'), and explains the organizing principle: tabs show different contexts of the same subject with examples like programming language, OS, or skill level. This clearly distinguishes it from sibling card tools such as create_table_card, create_chart_card, or create_video_card.

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 provides clear context for when this card is appropriate: when the user needs multiple perspectives on the same subject, e.g., one tab per language or OS. It does not explicitly name alternatives or state when not to use it, but the use case is specific enough for an agent to route correctly.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/jhauga/mcp-chat-cards'

If you have feedback or need assistance with the MCP directory API, please join our Discord server