Skip to main content
Glama
dzintars2

candlestick-chart-mcp

by dzintars2
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Local

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlapping purposes. The tool's purpose is clearly defined and distinct.

    Naming Consistency5/5

    The single tool name follows a clear verb_noun pattern (render_candlestick_chart), which is consistent and descriptive. There are no other tools to create inconsistencies.

    Tool Count3/5

    A single tool feels thin and borderline for a typical MCP server, even though the tool itself is comprehensive and covers the charting domain well. It sits at the lower boundary of the acceptable range.

    Completeness5/5

    The tool appears to fully cover the domain of candlestick chart rendering, supporting multiple chart types, overlays, markers, and themes. No obvious gaps in the server's stated purpose.

  • Average 4.4/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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?

    The description discloses important behavioral traits: it returns a PNG image and makes no network calls. This goes beyond the existing readOnly and idempotent annotations, and does not contradict them. The color scheme default and override hint also add transparency.

    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 three concise sentences, front-loaded with the core purpose. Every sentence adds value—drawing, features, and network behavior—without unnecessary fluff 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?

    Given the tool's complexity (22 parameters, nested objects) and absence of an output schema, the description covers the essential context: return type (PNG) and network behavior. However, it does not specify whether the PNG is returned as base64, a URL, or raw bytes, which is a minor gap for a tool with no output schema.

    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?

    The schema already covers 86% of parameters in detail, so a baseline of 3 applies. The description adds a high-level feature overview (volume panel, moving averages, Bollinger, VWAP, etc.) that maps to parameters, and specifically advises on the 'classic' color scheme, which provides useful guidance 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 draws candlestick charts (with Heikin-Ashi and OHLC-bar variants) from OHLCV data and returns a PNG. It enumerates supported features, leaving no ambiguity about the tool's purpose.

    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 on what the tool does and that it makes no network calls, but it does not explicitly state when to use it over alternatives. Since no sibling tools are listed, this is acceptable, yet explicit usage guidance is missing.

    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

candlestick-chart-mcp MCP server

Copy to your README.md:

Score Badge

candlestick-chart-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/dzintars2/candlestick-chart-mcp'

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