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Extract-Antv-Topic

query_antv_document

query_antv_document

Fetch AntV documentation, code examples, and best practices for implementing features, debugging issues, or learning about AntV libraries like g2, g6, l7, x6, f2, s2, g, ava, and adc.

Instructions

AntV Context Retrieval Assistant - Fetches relevant documentation, code examples, and best practices from official AntV resources. Supports g2, g6, l7, x6, f2, s2, g, ava, adc libraries, and handles subtasks iterative queries.

MANDATORY: Must be called for ANY AntV-related query (g2, g6, l7, x6, f2, s2, g, ava, adc), regardless of task complexity. No exceptions for simple tasks.

When to use this tool:

  • Implementation & Optimization: To implement new features, modify styles, refactor code, or optimize performance in AntV solutions.

  • Debugging & Problem Solving: For troubleshooting errors, unexpected behaviors, or technical challenges in AntV projects.

  • Learning & Best Practices: To explore official documentation, code examples, design patterns, or advanced features.

  • Complex Task Handling: For multi-step tasks requiring subtask decomposition (e.g., "Build a dashboard with interactive charts").

  • Simple modifications: Even basic changes like "Change the chart's color" or "Update legend position" in AntV context.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
libraryYes
queryYes
topicYes
intentYes
tokensNo
subTasksNo
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context about handling iterative queries and subtask decomposition, but lacks details on rate limits, authentication needs, error handling, or response format. The description doesn't contradict annotations (none exist), but it's incomplete for a tool with 6 parameters and no output schema.

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

Conciseness2/5

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

The description is overly verbose and repetitive, with redundant emphasis on mandatory usage and overlapping examples. Sentences like 'No exceptions for simple tasks' and the bulleted list could be condensed. It's front-loaded with purpose but loses efficiency in the detailed guidelines, making it longer than necessary for effective tool selection.

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

Completeness2/5

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

Given the tool's complexity (6 parameters, 0% schema coverage, no output schema, no annotations), the description is incomplete. It covers purpose and usage well but lacks parameter explanations, behavioral details (e.g., response format, error cases), and doesn't address how results are returned. For a retrieval tool with multiple inputs, this leaves significant gaps for an AI agent.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate for all 6 parameters. While it mentions 'library' (listing supported ones) and implies 'query' through usage examples, it doesn't explain 'topic', 'intent', 'tokens', or 'subTasks'. The description adds some meaning for 2 parameters but leaves 4 undocumented, failing to adequately compensate for the schema gap.

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 with specific verbs ('fetches relevant documentation, code examples, and best practices') and resources ('official AntV resources'), explicitly listing the supported libraries (g2, g6, l7, etc.). It distinguishes from the sibling tool 'extract_antv_topic' by emphasizing comprehensive retrieval rather than topic extraction.

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 usage guidelines with a mandatory call instruction for any AntV-related query, detailed scenarios (implementation, debugging, learning, complex tasks, simple modifications), and clear when-to-use examples. It implicitly distinguishes from alternatives by mandating its use for all AntV tasks, though it doesn't explicitly compare to the sibling tool.

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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