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Bishop81

wpgoldmine-mcp

by Bishop81
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a distinct purpose: finding opportunities, getting details for a single plugin, and listing presets. There is no overlap, and descriptions clearly differentiate them.

    Naming Consistency5/5

    All tool names use snake_case with a verb_noun pattern (find, get, list) and are consistent in style and structure.

    Tool Count4/5

    With only 3 tools, the set is minimal but appropriate for the niche domain of discovering plugin opportunities. The core functionality (discover, detail, preset list) is covered without unnecessary bloat.

    Completeness5/5

    The tools provide a complete workflow for the declared purpose: listing presets, finding opportunities using those presets or custom criteria, and retrieving detailed info on a specific plugin. No obvious gaps for discovery.

  • Average 4.2/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 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

  • 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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  • 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

  • Behavior3/5

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

    With no annotations provided, the description carries full burden. It indicates a read operation (Get) and lists returned fields, but does not disclose potential error handling (e.g., invalid slug), rate limits, or external dependency behavior beyond 'Powered by wpgoldmine.io'.

    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?

    Two sentences, front-loaded with the core purpose, followed by a source attribution. No wasted words; every part is meaningful.

    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?

    The description covers what the tool returns and how to use it. Minor gap: does not specify behavior when slug is not found or if data is unavailable. However, for a simple fetch tool, this is acceptable.

    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 the slug parameter described. The description adds the phrase 'by its WordPress.org slug' and an example, but these add minimal value beyond the schema's own description. Baseline score applies.

    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?

    Description explicitly states the verb 'Get' and resource 'opportunity snapshot for one plugin', lists specific data fields (installs, rating, etc.), and the title confirms 'Get plugin opportunity detail'. The sibling tools find_plugin_opportunities and list_opportunity_presets suggest different scopes (searching vs. listing), making this tool distinct.

    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 when a specific plugin slug is known, as it requires the slug parameter. However, it does not explicitly state when not to use it or mention alternatives like find_plugin_opportunities for searching.

    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?

    No annotations exist, so the description carries full burden. It discloses returns are 'ranked, capped list with opportunity scores' and mentions an external data source. However, it lacks details on rate limits, authentication needs, or data freshness, which would be helpful.

    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 sentences, front-loaded with purpose, then usage, then return value. No redundancy, every sentence 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?

    With 6 parameters and no output schema, the description explains what the tool returns (ranked, capped list with opportunity scores). It could mention potential limits like page size, but for a discovery tool this is adequate.

    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%, baseline 3. The description adds meaning by explaining that 'preset' provides a curated angle and composes with 'tag', which goes beyond the schema's basic property descriptions.

    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 finds WordPress/WooCommerce plugins that are commercial opportunities, with specific criteria (large user bases, abandonment, low ratings). It differentiates from siblings 'get_plugin' and 'list_opportunity_presets' by focusing on opportunity discovery.

    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 gives explicit usage guidance: 'Use a preset for a curated angle, and/or narrow by tag, install range, and max rating.' It doesn't explicitly state when not to use, but clarity on composition with presets and tags is sufficient.

    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?

    No annotations are provided, so the description carries the burden. It discloses that presets are curated queries powered by wpgoldmine.io, which adds transparency. However, it doesn't mention potential side effects like network dependency or whether results are cached.

    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?

    Two sentences with no redundancy. The first sentence gives concrete examples, the second explains purpose and source. Every word earns its place.

    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 zero-parameter list tool with no output schema, the description covers what it returns (named presets), their purpose, and data source. It is complete for an AI agent to understand and invoke 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 tool has zero parameters, so schema coverage is 100% by default. Baseline is 4. The description adds value by providing example preset names and linking to the sibling tool, clarifying how the output is used.

    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 'list' as the verb and 'opportunity presets' as the resource, with concrete examples (abandoned-plugins, low-rated-popular, beatable-niche). It differentiates from siblings by mentioning it's for passing to find_plugin_opportunities.

    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?

    Explicitly says these presets can be passed to find_plugin_opportunities, providing context for when to use this tool. Does not include exclusions or alternative scenarios, but for a simple list tool this is sufficient.

    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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