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lishireason

w5-pg-mcp

by lishireason
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.2.1

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap. The purpose of 'add' is clearly stated as adding two numbers.

    Naming Consistency5/5

    A single tool cannot exhibit naming inconsistency. The name 'add' is a simple, clear verb that matches its function.

    Tool Count1/5

    A single trivial arithmetic operation is severely under-scoped for a server named 'w5-pg-mcp', which implies a database-related purpose. The tool count is far too low for any meaningful functionality.

    Completeness1/5

    The tool surface is severely incomplete. Even as a math utility, it lacks basic operations like subtract, multiply, or divide, and it bears no relation to the apparent 'pg' (Postgres) domain implied by the server name.

  • Average 4.2/5 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

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  • This repository includes a README.md file.

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  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
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  ]
}

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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

  • Behavior4/5

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

    No annotations are provided, so the description carries the behavioral burden. 'Add two numbers' accurately and sufficiently communicates a pure arithmetic operation, with no indication of side effects or hidden behavior.

    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 four words long, contains zero filler, and conveys the complete operation. It is front-loaded and every word earns its place.

    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 two-integer addition tool with an output schema and no siblings, this description is nearly complete. It could mention the return value or pure behavior explicitly, but the output schema and inherent simplicity reduce that need.

    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 0%, so the description must compensate. It clarifies that both parameters are numbers to be added, but it does not name them or describe each parameter individually. Since addition is commutative, the lack of role differentiation is a minor gap.

    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 states a precise verb ('add') and a clear resource ('two numbers'), making the tool's purpose immediately obvious. With no sibling tools, there is no ambiguity to resolve.

    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 intended use is clear: this tool is for summing two numbers. It does not explicitly discuss when not to use it, but with no sibling tools and only two integer parameters, the context is straightforward.

    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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  • 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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w5-pg-mcp MCP server – quality and maintenance score on Glama

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