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BACH-AI-Tools

Semrush Keyword Magic Tool MCP Server

by BACH-AI-Tools
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation4/5

    keyword_overview_analysis and keyword_magic_tool are clearly distinct (one provides metrics for a specific keyword, the other generates suggestions). The questions tool is somewhat ambiguous in name but appears to target a specific keyword type, so it is distinguishable.

    Naming Consistency3/5

    Two tools share a 'keyword_' prefix, but the third is simply 'questions', breaking the pattern. There is no consistent verb_noun structure, making naming conventions mixed though still readable.

    Tool Count4/5

    Three tools is a reasonable number for a focused keyword research server. It covers the core capabilities without being bloated, though it is slightly sparse.

    Completeness4/5

    The set covers keyword overview and keyword suggestion discovery, which are central to the domain. Questions adds a specialized angle, but common operations like keyword filtering or clustering are missing, leaving minor gaps.

  • Average 2.2/5 across 3 of 3 tools scored. Lowest: 1.4/5.

    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 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.

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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

  • Behavior1/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It offers nothing beyond the word 'Questions', with no mention of side effects, permissions, return behavior, or other operation characteristics.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness2/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is extremely short, which could be seen as concise, but it under-specifies and offers zero actionable content. It is not effectively structured or front-loaded; it is merely a placeholder.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness1/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given no annotations, no output schema, and only two parameters with example values, the description is far from complete. It fails to explain the tool's purpose, return values, or relationship to sibling tools, making it inadequate for an AI agent.

    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 100% because both parameters have description fields, even though those are only 'Example value' strings. Per the baseline rule for high coverage, a 3 is warranted. The description itself adds no parameter semantics, but the schema provides minimal example-based hints.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose1/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    Tautological: description restates name/title.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines1/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    No guidance is provided on when to use this tool versus alternatives. The description lacks any mention of context, prerequisites, or exclusions, leaving the agent without a basis for selecting it.

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

  • Behavior1/5

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

    With no annotations provided, the description carries the full burden of explaining behavioral traits. It merely states 'analysis' with no details on whether it is read-only, what side effects exist, what the output format is, or any constraints. This is even less informative than typical mutation descriptions.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness2/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is extremely short, but this is under-specification, not effective conciseness. It does not include essential structural elements like purpose, expected input semantics, or output behavior, making it insufficient for an agent to use correctly.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness1/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool has only two params and no output schema or annotations, the description still fails to provide minimal context. It does not explain what kind of analysis is performed, what the result looks like, or how it relates to sibling tools, leaving the agent completely uninformed.

    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 100%, so the baseline is 3. The description itself adds no extra meaning beyond the schema, but the example values in the schema provide some format guidance. The description does not enhance the semantic understanding of 'keyword' or 'country' beyond what is already present.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose2/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description 'Keyword Overview analysis' essentially restates the tool name without a specific verb or resource. It does not distinguish from sibling tools like keyword_magic_tool or questions, and it lacks actionable clarity about what the tool actually does.

    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?

    The description provides no guidance on when to use this tool versus alternatives. It does not mention any context, prerequisites, or exclusions, leaving the agent without direction for tool selection.

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

  • Behavior2/5

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

    With no annotations, the description carries the full burden of behavioral disclosure. It states the tool returns 'a list of best keywords' but does not disclose how results are sorted, any rate limits, or limitations. The read-only nature is implied but not stated, which is a gap for an agent.

    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 two sentences with no fluff. It front-loads the core value proposition ('Find millions of keyword suggestions') and immediately states the input/output. Every word earns its place.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    The tool has no output schema and no annotations, so the description must sufficiently explain usage and returns. While it mentions generating a keyword list, it omits the required 'country' parameter, which could mislead an agent into thinking only a topic is needed. The description is not complete enough for confident invocation.

    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?

    The schema provides 100% coverage with example values for all parameters, giving a baseline of 3. The description adds only the term 'topic' for the 'keyword' parameter and says nothing about the required 'country' or optional 'languagecode' parameters, so it does not enhance meaning 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 tool's function: 'Find millions of keyword suggestions for your SEO.' It uses a specific verb ('Find') and resource ('keyword suggestions'), and the action is distinct from siblings like 'keyword_overview_analysis' (analysis) and 'questions' (question generation).

    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 implies usage for keyword research ('Enter a topic and get your list of best keywords'), but provides no explicit guidance on when to use this tool versus the sibling tools. No exclusions or alternatives are mentioned, so it earns an 'implied usage' score rather than a clearer contextual guideline.

    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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