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SpringDamon

mcp-server-dameng

by SpringDamon
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.6

  • Disambiguation5/5

    Each tool has a distinct purpose: describe_table retrieves column details for a specific table, list_tables enumerates all user tables, and query_dameng executes arbitrary SQL. There is no overlap or ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case: describe_table, list_tables, query_dameng. The naming is uniform and predictable.

    Tool Count4/5

    With 3 tools, the server is slightly lean but still well-scoped for its purpose of exploring a Dameng database. Each tool serves a necessary function without redundancy.

    Completeness5/5

    The tool set covers the core read operations for a database: listing tables, describing table structure, and executing queries. For a read-only exploration server, it is complete with no obvious gaps.

  • Average 4.1/5 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 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.

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

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

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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

  • Behavior3/5

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

    No annotations are provided, so the description bears full responsibility. It discloses that the tool returns table names, comments, and row count estimates, which is helpful. However, it does not mention any side effects, prerequisites, or performance considerations. The disclosure is moderate.

    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, concise sentence that captures the core functionality without any unnecessary words. It is front-loaded and efficient.

    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 simple listing tool without an output schema, the description adequately conveys what the tool returns (names, comments, row count estimates). It does not cover edge cases or error scenarios, but it is complete enough for its intended use.

    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 coverage is 100% with one parameter (schema) already described clearly in the input schema. The tool description does not add further meaning beyond the schema, so the baseline score of 3 is appropriate.

    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 lists all user tables in the Dameng database, including table names, comments, and estimated row counts. It uses a specific verb ('list') and resource ('tables'), and the purpose is distinct from sibling tools like describe_table (which likely describes a specific table) and query_dameng (for arbitrary queries).

    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 does not provide explicit guidance on when to use this tool versus alternatives like describe_table or query_dameng. The purpose is implied, but no exclusion criteria or usage context is given, making it adequate but not proactive.

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

  • Behavior3/5

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

    With no annotations, the description carries full burden. It mentions DML (modification), which hints at destructive potential, but does not disclose side effects, authentication requirements, or error behavior. This is adequate but not detailed.

    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?

    A single, concise sentence that includes the key elements: action, resource, supported operations, and output type. No superfluous words.

    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 low complexity (2 parameters, no output schema, no nested objects), the description is largely complete. It covers the essential purpose and return type. However, it could mention limit default or error handling for a truly complete picture.

    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 coverage is 100%, so description adds value by clarifying the tool supports both SELECT and DML, which is not explicit in the schema's parameter descriptions. The description also notes the return type '结构化结果' (structured results), providing context 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 verb 'execute' and resource 'Dameng database SQL queries', explicitly listing supported SQL types (SELECT/DML). This distinguishes it from sibling tools describe_table and list_tables, which focus on metadata.

    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?

    No guidance is given on when to use this tool versus the sibling tools. For a tool that executes arbitrary SQL, it would be helpful to note that it's for general queries, while describe_table and list_tables are for schema inspection.

    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?

    No annotations are provided, so the description carries full behavioral disclosure. It specifies the returned fields (column name, type, nullable, default, comment), which is sufficient for a read-only metadata tool. It does not mention potential errors or performance, but the simplicity of the operation makes this acceptable.

    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?

    A single sentence that encapsulates the tool's purpose and output, with no wasted words. It is front-loaded with the main action and outcome.

    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 simple metadata query tool with two parameters and no output schema, the description provides complete context: what the tool does, what parameters are needed, and what information is returned. No gaps remain.

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

    Parameters5/5

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

    Schema coverage is 100%, and the description adds extra semantic value: it notes that tableName is case-insensitive and that schema defaults to the current user's schema. This goes beyond the schema's basic description.

    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: viewing column information (name, type, nullable, default, comment) of a Dameng database table. It effectively distinguishes from sibling tools 'list_tables' (lists tables) and 'query_dameng' (queries data).

    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 usage for examining table schema, but does not explicitly state when to use it over siblings or provide alternative guidance. However, the context of sibling names makes the differentiation clear.

    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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