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openbot-coder

quantdb-mcp

by openbot-coder
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Python
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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 a single tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as executing read-only SQL queries against a specified set of tables, so agents can directly know what it does.

    Naming Consistency5/5

    The sole tool is named 'query', which is a simple, predictable, and consistent name. Since there is only one tool, there are no mixed conventions or inconsistent patterns to confuse an agent.

    Tool Count4/5

    A single tool might seem thin, but it serves a comprehensive purpose as a generic SQL query interface covering many tables. While additional metadata tools could be useful, the count is slightly under but acceptable for a focused read-only database server.

    Completeness4/5

    The tool provides complete read-only access to all listed tables, supporting SELECT/WITH queries with a row limit. A minor gap is the lack of schema/metadata exploration tools, but the description partially fills this by listing the available tables, making it workable for most use cases.

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

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

    • No community issues in the last 6 months
    • 3 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

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  • This repository includes a README.md file.

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

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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

  • Behavior5/5

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

    No annotations are provided, but the description fully compensates by disclosing read-only behavior, statement restrictions, and the row limit. It also clarifies supported tables and the ODS prefix, which are important operational context. This exceeds typical descriptions.

    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?

    Three terse sentences carry all key information: read-only, supported tables, and constraints. No filler or 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 single-parameter SQL tool with an output schema, the description covers the essential operational aspects: target database, allowed query forms, table names, and result limits. There are no obvious gaps that would prevent correct invocation.

    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?

    The schema only defines sql as a string with no description (0% coverage). The description adds crucial semantics: it defines the acceptable SQL dialect (single SELECT/WITH), enumerates permissible tables, and states the 10,000-row cap. This substantially compensates for the sparse 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 uses a specific verb ('执行只读 SQL 查询' – execute read-only SQL queries) and identifies the resource ('量化数据库' – quantitative database), clearly distinguishing it as a query tool. It goes beyond a tautology by specifying supported tables and constraints.

    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?

    It provides explicit usage boundaries: only single SELECT/WITH statements are allowed, write and multi-statement queries are rejected, and result size is limited to 10,000 rows. It also enumerates supported tables, giving the agent clear criteria for when this tool applies. No sibling tools exist, so alternative tool guidance is not applicable.

    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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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.

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