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CSOAI-ORG

Keyword Extractor AI MCP

Server Quality Checklist

83%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool targets a distinct aspect of keyword analysis: density, comparison, extraction, and tagging. No two tools have overlapping purposes, making it easy for an agent to select the correct one.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern using snake_case (analyze_density, compare_keywords, extract_keywords, suggest_tags). No mixing of conventions.

    Tool Count5/5

    With 4 tools, the server is tightly scoped to keyword extraction and analysis. Each tool serves a clear need without bloat or deficiency.

    Completeness4/5

    The toolset covers the main keyword workflows: extraction, density, comparison, and tagging. A minor gap is the lack of a tool for keyphrase extraction or batch processing, but it's adequate for its purpose.

  • Average 4.1/5 across 4 of 4 tools scored.

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

    • No community issues in the last 6 months
    • 15 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing
  • 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.

  • This repository includes a glama.json configuration file.

  • This server has been verified by its author.

  • Add related servers to improve discoverability.

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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?

    A dedicated 'Behavioral Transparency' section covers side effects (read-only), authentication, rate limits, error handling, idempotency, and data privacy comprehensively. Since no annotations are provided, the description fully carries the burden, and it does so excellently.

    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 well-structured with clear sections but is verbose, repeating some behavioral info (e.g., read-only appears in both the initial behavior note and the transparency section). Some sentences could be trimmed without losing meaning.

    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 purpose, usage, behavior, and transparency thoroughly. It assumes an output schema exists (which it does), so omission of return value details is acceptable. Minor gaps: no explanation of how to include keywords (e.g., comma-separated) or what the recommendations format looks like.

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

    Parameters2/5

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

    The description lists the three parameters but adds minimal meaning: e.g., 'text (str): The text to analyze or process' merely repeats the name and type. With 0% schema description coverage, the description fails to compensate by explaining formats or relationships, leaving agent confusion about how to specify target_keywords or api_key.

    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 calculates keyword density for SEO analysis with target keyword tracking and recommendations. This specific verb+resource combination distinguishes it from siblings like compare_keywords, extract_keywords, and suggest_tags.

    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 includes explicit 'When to use' and 'When NOT to use' sections, providing context for structured analysis and caution against real-time decision-making without human review. However, it does not directly compare with sibling tools.

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

  • Behavior5/5

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

    A dedicated 'Behavioral Transparency' section details side effects (none), authentication (none for basic), rate limits (10/day free), error handling (structured errors), idempotency (fully), and data privacy (no storage). This goes well beyond what annotations would provide, fully informing an AI agent of all behavioral traits.

    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 well-organized into sections but contains redundancy: the 'Behavior:' section and 'Behavioral Transparency' section overlap significantly, repeating similar information. It could be more concise by merging these sections.

    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 tool's complexity (comparing keywords, multiple parameters, output schema), the description covers most relevant aspects: behavior, usage guidance, error handling, and rate limits. However, it lacks explanation of the output format or examples, which would be helpful for an agent to properly interpret results.

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

    Parameters2/5

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

    With 0% schema coverage, the description must compensate but fails: it merely repeats parameter names with trivial descriptions like 'The text a to analyze or process.' No additional semantics, constraints, examples, or formatting details are provided for any of the three parameters.

    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 first sentence clearly states the tool's purpose: 'Compare keywords between two texts to find common, unique, and differentiating terms.' This is a specific verb and resource, and it differentiates from siblings like 'extract_keywords' or 'suggest_tags' which focus on extraction or suggestion rather than comparison.

    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 includes explicit 'When to use' and 'When NOT to use' sections, advising for structured analysis and against use in real-time production without review. However, it does not mention specific alternatives among siblings, and the guidance is somewhat generic, lacking concrete scenarios where this tool is preferred.

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

  • Behavior5/5

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

    Despite no annotations, the description comprehensively details side effects (read-only, no side effects), authentication requirements, rate limits, error handling, idempotency, and data privacy. This fully informs the agent of behavioral traits.

    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 well-structured with clear sections and front-loads the core purpose. However, it is somewhat verbose, especially the parameter descriptions that could be condensed. Overall, it organizes information effectively.

    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 behavioral aspects thoroughly but omits explanation of the output format, despite an output schema existing. It also lacks parameter-level constraints. Given the complexity and lack of annotations, it is nearly complete but missing output details.

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

    Parameters2/5

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

    The 'Args' section simply repeats the parameter names with generic descriptions like 'The text to analyze or process.' for each, adding no meaningful constraints or context beyond the schema. With 0% schema coverage, the description fails to compensate, leaving parameters poorly documented.

    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 extracts top keywords using TF-IDF, stop word filtering, and frequency analysis. It distinguishes itself from siblings like analyze_density and compare_keywords by focusing on extraction rather than comparison or density analysis.

    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 includes explicit 'When to use' and 'When NOT to use' sections, providing guidance on appropriate contexts. However, it does not directly compare to sibling tools, and the 'When to use' text is somewhat generic, not specifically tied to keyword extraction.

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

  • Behavior5/5

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

    The description thoroughly discloses behavioral traits: read-only, stateless, idempotent, authentication requirements, rate limits, error handling, and data privacy. Since no annotations are provided, the description fully carries the burden and does so exceptionally.

    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 well-structured with clear sections but contains redundancy (behavior details repeated in both the 'Behavior' paragraph and the 'Behavioral Transparency' section). It could be more concise.

    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 output schema exists, the description covers behavior, usage, and error handling comprehensively. The weak parameter descriptions are a minor gap, but overall completeness is strong.

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

    Parameters2/5

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

    The Args section repeats parameter names with trivial descriptions (e.g., 'text (str): The text to analyze or process.'), adding no meaningful insight beyond the schema titles. With 0% schema description coverage, the description fails to compensate adequately.

    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: 'Suggest tags and categories for content based on keyword analysis and topic matching.' This provides a specific verb and resource, and distinguishes it from sibling tools like analyze_density or extract_keywords.

    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 includes explicit 'When to use' and 'When NOT to use' sections, giving clear context and exclusion criteria (not suitable for real-time production without human review). However, it does not mention alternatives or sibling tools.

    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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