Skip to main content
Glama
ryuk3nd0

echarts-mcp

by ryuk3nd0
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    render_echart, validate_echart_option, and list_chart_types each address a distinct concern: output generation, option validation, and capability discovery. No two tools overlap in purpose, so agent selection should be straightforward.

    Naming Consistency4/5

    All tool names use snake_case with a verb-first pattern: render_, validate_, list_. The minor inconsistency is the object noun varying between echart, echart_option, and chart_types, but the overall convention is still predictable.

    Tool Count5/5

    Three tools cover a narrow, well-defined pipeline: discovering chart types, validating options, and rendering output. This is an appropriate scope with no redundant tools and no obvious missing boilerplate steps.

    Completeness4/5

    The core workflow is well covered: list_chart_types aids discovery, validate_echart_option prevents render failures, and render_echart produces several output formats. However, render_echart's description references an exportToUrl tool for chat platforms that is not actually part of this server, creating a minor gap for agents aiming at Slack/Discord/Telegram.

  • Average 4.4/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 8 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under Apache 2.0.

  • 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 the burden of behavioral disclosure. It clearly states it is a dry-run (no rendering) and returns errors and warnings, which are meaningful behavioral traits. It does not detail edge cases or side effects, but the core behavior is transparent.

    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 two sentences long, front-loaded with the key action ('Dry-run structural and semantic validation') and includes the return value summary. Every word contributes to the user's understanding with no fluff.

    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 single-parameter tool with no annotations and no output schema, the description effectively covers the tool's purpose, behavioral constraints, and return semantics. It could be more complete by listing example validation rules or the exact return shape, but it is largely sufficient for an AI agent to decide when and how to invoke it.

    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?

    The schema already fully describes the only parameter 'option' with a clear description, so baseline is 3. The tool description adds the version context (echarts@6.1.0) and validation scope (structural/semantic) but does not add additional parameter-level meaning beyond 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 performs 'structural and semantic validation of an ECharts option against echarts@6.1.0 without rendering.' It uses a specific verb ('validates') and resource ('ECharts option'), and the 'without rendering' clause distinguishes it from the sibling render_echart tool.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description implies usage context by noting 'without rendering' and 'dry-run', suggesting it is the safe pre-render check. However, it never explicitly mentions alternative tools or provides when-to-use vs. when-not-to-use guidance relative to siblings render_echart and list_chart_types.

    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, the description carries the burden of behavioral disclosure. It reveals a key trait: the list is not exhaustive and render_echart accepts any ECharts v6 option, which prevents misuse. It doesn't describe the return format, but for a simple list tool this is sufficient.

    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?

    Two sentences, front-loaded with the main purpose, and every clause adds value. The second sentence prevents a common misconception about allowlisting. No redundancy.

    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?

    For a zero-parameter, no-output-schema tool, the description fully covers what an agent needs to know: what it does, how it relates to render_echart, and its non-exhaustive nature. Nothing is missing.

    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?

    There are zero parameters, so the schema is trivially complete. The description doesn't need to add parameter details. Baseline of 4 applies, and the description's mention of ECharts v6 context is a small bonus.

    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 ('Lists') and resource ('ECharts series types'), clearly stating the tool's purpose. It also distinguishes itself from sibling tools by noting that render_echart accepts any option, clarifying that this list is for discoverability only.

    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 implies when to use this tool (for agent discoverability) and explicitly contrasts with render_echart by stating this is not an allowlist. It doesn't mention validate_echart_option or explicitly state when not to use it, but the context is clear enough.

    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?

    Annotations are absent, so description carries the load. It discloses the server-side behavior, output options, optional dependency for PNG, and the exportToUrl alternative. It doesn't mention failure modes or performance characteristics, but given the context, that's acceptable. No contradictions.

    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?

    Concise, informative three sentences. Every word earns its place.

    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 complexity (rendering, formats, dependency), description covers key aspects: server-side, output types, dependency requirement, and alternative for chat platforms. Output schema exists, so return values are covered.

    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 covers all 9 parameters with descriptions (100% coverage). The description adds the dependency caveat for PNG and the chat-platform alternative, which are beyond schema. So it adds value above the baseline 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 renders Apache ECharts visualizations server-side outputting PNG/SVG/HTML formats. It distinguishes itself from sibling tools like validate_echart_option (validation) and listing chart types by focusing on rendering.

    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?

    Explicitly states when to use exportToUrl for chat platforms (Slack/Discord/Telegram) as an alternative, which is a clear usage guideline. It also notes the requirement for hosting configuration, giving strong contextual guidance.

    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

echarts-mcp MCP server

Copy to your README.md:

Score Badge

echarts-mcp 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/ryuk3nd0/echarts-mcp'

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