Skip to main content
Glama

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: sun_get_summary retrieves content of a specific file, sun_list_summaries lists all available summaries, and sun_summarize creates and saves a new summary. There is no overlap in functionality, making tool selection unambiguous for an agent.

    Naming Consistency5/5

    All tools follow a consistent 'sun_verb_noun' pattern with snake_case, using descriptive verbs like 'get', 'list', and 'summarize'. This uniformity makes the tool set predictable and easy to understand.

    Tool Count5/5

    With 3 tools, the server is well-scoped for session summary management, covering creation, listing, and retrieval. Each tool earns its place without being overly sparse or bloated, fitting typical use cases efficiently.

    Completeness4/5

    The tools provide good coverage for the session summary domain, including create (summarize), read (get_summary), and list operations. A minor gap exists in update or delete functionality, but agents can likely work around this for basic workflows.

  • Average 2.8/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 0 commits 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
  • 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

  • Behavior2/5

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

    With no annotations provided, the description carries full burden for behavioral disclosure. It mentions saving as a .mdc file, which implies a write operation, but doesn't specify where it's saved, whether it overwrites existing files, what permissions are needed, or what happens on failure. For a tool that creates files, this leaves critical behavioral aspects undocumented.

    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 brief (two sentences) but contains confusing elements: the '-sun command' reference seems misplaced for an AI tool interface, and the second sentence repeats language information already in the schema. While not verbose, it includes content that doesn't effectively serve its purpose.

    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?

    For a tool that creates files with 4 parameters and no annotations or output schema, the description is inadequate. It doesn't explain the summary format, file naming conventions, error conditions, or how this tool relates to its siblings. The absence of output schema means the description should address return values, which it doesn't.

    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 schema already fully documents all 4 parameters. The description adds no additional parameter information beyond what's in the schema, not explaining relationships between parameters or providing usage examples. This meets the baseline for high schema coverage.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description states the tool 'summarizes current session and saves as .mdc file', which provides a clear verb+resource combination. However, it doesn't differentiate from sibling tools like 'sun_get_summary' or 'sun_list_summaries', leaving ambiguity about when to use this versus retrieving existing summaries.

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

    Usage Guidelines2/5

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

    The description mentions using '-sun command' for Chinese or '-sun en' for English, but this appears to be command-line syntax rather than guidance for AI agent usage. It provides no explicit when-to-use instructions, no exclusions, and no comparison to sibling tools, offering minimal practical guidance.

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

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'Get content', implying a read-only operation, but does not specify if it requires authentication, has rate limits, returns errors for missing files, or details the output format (e.g., text, JSON). This leaves significant gaps in understanding how the tool behaves beyond basic retrieval.

    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 a single, straightforward sentence: 'Get content of a specific summary file'. It is front-loaded and wastes no words, making it efficient. However, it could be slightly more informative without losing conciseness, such as hinting at the file type or source.

    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 simplicity (1 parameter, no output schema, no annotations), the description is incomplete. It does not explain what a 'summary file' is, how content is returned, or potential errors. For a retrieval tool, this lack of context makes it harder for an agent to use correctly without trial and error.

    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 input schema has 100% description coverage, with the 'filename' parameter clearly documented. The description adds no additional meaning beyond the schema, such as examples of valid filenames or constraints. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description does not compensate but also does not detract.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description states the tool 'Get content of a specific summary file', which clearly indicates it retrieves content from a file. However, it lacks specificity about what a 'summary file' entails (e.g., format, source) and does not differentiate from siblings like 'sun_list_summaries' (which likely lists files) or 'sun_summarize' (which might generate summaries). This makes the purpose somewhat vague but understandable.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus its siblings. It does not mention prerequisites, such as needing to know the filename from 'sun_list_summaries', or alternatives like using 'sun_summarize' for creating summaries instead of retrieving them. Without any usage context, the agent must infer this from tool names alone.

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

  • Behavior2/5

    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 states the action ('List') but doesn't describe traits like whether this is a read-only operation, if it requires permissions, how results are returned (e.g., pagination, sorting), or potential side effects. This leaves significant gaps for a tool that interacts with saved data.

    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, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded with the core action and resource, making it easy to parse quickly, and there is no wasted verbiage.

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

    Completeness3/5

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

    Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate. However, it lacks context about the return format (e.g., list structure, summary fields) and behavioral aspects like error handling or data access constraints, which could be important for an AI agent to use it correctly.

    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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate here. A baseline of 4 is applied as it adequately handles the lack of parameters without introducing confusion.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the verb ('List') and resource ('all saved session summaries'), making the purpose unambiguous. It doesn't explicitly differentiate from sibling tools like 'sun_get_summary' (which likely retrieves a specific summary) or 'sun_summarize' (which likely creates summaries), but the scope 'all saved' provides some implicit distinction.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus alternatives like 'sun_get_summary' or 'sun_summarize'. It lacks context about prerequisites, such as whether summaries must exist or if authentication is needed, and offers no explicit when-not-to-use scenarios or comparisons.

    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

sun-mcp MCP server

Copy to your README.md:

Score Badge

sun-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/ChenYCL/sun-mcp'

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