Skip to main content
Glama
antvis

MCP Server AntV

Official
by antvis
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Local

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: extract_antv_topic handles preprocessing, requirement parsing, and intent extraction, while query_antv_document focuses on retrieving documentation and solutions. Their mandatory sequencing (extract first, then query) reinforces this separation, eliminating any ambiguity about when to use each tool.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with snake_case naming: extract_antv_topic and query_antv_document. The naming clearly indicates their functions (extract vs. query) while maintaining domain specificity (antv_topic vs. antv_document), creating a predictable and readable convention throughout the set.

    Tool Count3/5

    With only 2 tools, the server feels thin for covering the broad AntV visualization domain (9 libraries mentioned). While the tools logically separate preprocessing from retrieval, many visualization tasks might require additional operations like code generation, validation, or specific library interactions that aren't represented in this minimal set.

    Completeness2/5

    The tool surface is severely incomplete for the AntV visualization domain. While the tools cover preprocessing and documentation retrieval, there are significant gaps: no tools for actual code generation, chart rendering, data transformation, style modification, or error handling. Agents will hit dead ends when trying to implement solutions beyond documentation lookup.

  • Average 4.3/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • 1 of 1 community issues answered or closed in the last 6 months
    • 0 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior4/5

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

    With no annotations provided, the description carries full burden and delivers substantial behavioral context. It discloses the tool's scope (supports 9 specific libraries), capability (handles subtasks iterative queries), and mandatory nature. However, it doesn't mention rate limits, authentication needs, or potential side effects, leaving some behavioral aspects unspecified.

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

    Conciseness3/5

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

    The description is appropriately front-loaded with the core purpose, but contains some redundancy (repeating the library list and emphasizing mandatory usage multiple times). The bulleted usage guidelines are well-structured but could be more concise. Overall, it's comprehensive but could benefit from tighter editing.

    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?

    Given the tool's complexity (6 parameters, no annotations, no output schema), the description provides substantial context about when and how to use it, its scope, and relationship with sibling tools. However, it doesn't describe what the tool returns (format, structure, or content), which is a significant gap since there's no output schema to compensate.

    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?

    With 100% schema description coverage, the baseline is 3. The description adds some context by mentioning 'supports subtasks iterative queries' which relates to the 'subTasks' parameter, and references 'extract_antv_topic' for parameter extraction, but doesn't provide significant additional parameter semantics beyond what's already documented in the schema.

    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 ('from official AntV resources'). It explicitly distinguishes from its sibling tool 'extract_antv_topic' by mentioning it as a source for parameters, establishing a clear functional relationship.

    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, comprehensive usage guidelines with a mandatory directive ('Must be called for ANY AntV-related query') and detailed when-to-use scenarios across five categories (implementation, debugging, learning, complex tasks, simple modifications). It clearly distinguishes from alternatives by making this the required tool for all AntV contexts.

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

  • Behavior4/5

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

    With no annotations provided, the description carries full burden and does well by describing key behavioral features: 'Smart Library Detection' (scans dependencies), 'Topic & Intent Extraction,' 'Task Complexity Handling' (decomposes tasks), and 'Seamless Integration' (prepares data for next tool). However, it doesn't mention potential limitations, error handling, or performance characteristics.

    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 clear sections (mandatory rule, usage scenarios, key features) and front-loaded with the most important information. However, some sentences could be more concise, and the 'Key features' section contains marketing language ('intelligently,' 'seamless') that doesn't add operational clarity.

    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?

    For a preprocessing tool with 3 parameters, 100% schema coverage, and no output schema, the description provides good context about its role in the workflow and behavioral characteristics. However, without annotations or output schema, it could benefit from more detail about what the tool actually produces (structured data format) and any limitations or error conditions.

    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 100%, so the baseline is 3. The description adds some context about 'library' parameter ('automatically detect project dependencies and intelligently recommend') and implies 'maxTopics' relates to 'complex tasks,' but doesn't provide additional semantic meaning beyond what's already well-documented in the schema descriptions.

    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 as 'intelligently identifying, parsing, and structuring user visualization requirements' for AntV libraries, specifying it's a 'preprocessing tool' and 'first step in processing AntV technology stack issues.' It explicitly distinguishes from its sibling 'query_antv_document' by stating it 'always precedes' that tool.

    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 rules with 'MANDATORY: Must be called for ANY new AntV-related queries' and a detailed 'When to use this tool' section listing four specific scenarios. It clearly states the tool must precede 'query_antv_document' and provides examples of what constitutes AntV-related queries.

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

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

mcp-server-antv MCP server

Copy to your README.md:

Score Badge

mcp-server-antv MCP server

Copy to your README.md:

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/antvis/mcp-server-antv'

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