Skip to main content
Glama
eugenregehr

OctoBoost SEO MCP Server

by eugenregehr

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.1.1

  • Disambiguation5/5

    Each tool has a clear, non-overlapping purpose: analyze runs analyses, get_stored_analysis retrieves past results, list_analyzers shows available options, and scan_domain discovers URLs. No ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern using snake_case, e.g., get_stored_analysis, list_analyzers. The analyze tool is slightly terse but fits the pattern.

    Tool Count5/5

    Four tools is appropriate for a focused SEO server. Each tool is essential and covers the core workflow without unnecessary bloat.

    Completeness4/5

    The server covers the main SEO analysis flow: scanning, analyzing, retrieving, and listing. Minor gaps exist, such as missing a tool to delete stored analyses, but core functionality is complete.

  • Average 4.3/5 across 4 of 4 tools scored. Lowest: 3.6/5.

    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 status not available
  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • 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

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden. It only states 'Crawl a domain' without disclosing behavioral traits such as potential impact on the target, rate limits, permissions required, or whether it stores data. This is insufficient for transparency.

    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 extremely concise with two short sentences. The first sentence captures the core function, and the second provides usage context. Every word is purposeful, with no fluff.

    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?

    Given the tool has 5 parameters including boolean flags and patterns, the description lacks details on how these affect crawling results. It does not describe the output format or any limitations (e.g., crawl depth). The omission of behavioral context and return value specification makes it incomplete for a complex tool.

    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 input schema has 100% description coverage for all 5 parameters, so the description does not need to add extra meaning. It adds no further details about parameters beyond the schema, maintaining the baseline.

    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 explicitly states the action ('crawl a domain') and the output ('SEO-relevant page URLs'). It distinguishes this tool from sibling tools (analyze, get_stored_analysis, list_analyzers) by specifying it is for discovery before analysis. Thus, purpose is clear and specific.

    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 says to use this 'to discover pages before running analysis', providing a clear contextual use case. However, it does not mention when not to use it or compare directly to sibling alternatives, lacking explicit exclusions.

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

  • Behavior4/5

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

    With no annotations, the description carries the burden of behavioral disclosure. It mentions no credit consumption, supports filtering, and returns paginated results (10 per page). These are key behavioral traits for a read-only retrieval tool.

    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 concise sentences, front-loaded with purpose, no wasted words. Every sentence adds value.

    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 retrieval tool with 4 optional parameters and no output schema, the description covers pagination, credit consumption, and filtering. It is complete enough given the tool's simplicity.

    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 individual parameter descriptions. The description adds a summary of filtering capabilities but does not provide new meaning beyond the schema. Baseline 3 is appropriate.

    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 verb 'Retrieve' and the resource 'previously stored SEO analysis reports', making the purpose unambiguous. It distinguishes itself from siblings like 'analyze' (new analysis) and 'scan_domain' (scanning) by focusing on stored reports.

    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 explicitly says 'No credits consumed' and 'Use this to review past analyses without re-crawling', indicating when to use. It does not explicitly exclude other tools but provides clear context for appropriate usage.

    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?

    No annotations provided, so the description fully discloses behavior: sequential processing, credit cost per URL, progress reporting, and detailed return values including GEO/AEO score with sub-metrics.

    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 concise with two sentences, front-loading the purpose and key details. Every sentence adds value without 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?

    Given one parameter and no output schema, the description adequately covers the tool's purpose, processing behavior, output contents, and constraints (credit cost). No missing critical information.

    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 description adds context beyond the schema (100% coverage) by explaining processing mode and cost per URL. While the schema defines the 'urls' parameter, the description clarifies its usage behavior.

    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 analyzes URLs for SEO issues, runs all analyzers, and returns scores, issues, and GEO/AEO scores. It distinguishes itself from siblings like get_stored_analysis (retrieves stored), list_analyzers (lists), and scan_domain (domain scan).

    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 indicates typical usage: analyzing one or more URLs fully. It mentions sequential processing and progress reporting. However, it doesn't explicitly state when to avoid this tool or provide direct comparisons to siblings.

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

  • Behavior4/5

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

    With no annotations provided, the description takes on the full burden. It clearly indicates a read-only listing operation with no mention of side effects. While simple, it adequately conveys the tool's 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?

    Two sentences: first states what it does, second provides usage guidance. No wasted words, front-loaded with the core purpose.

    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?

    Given zero parameters and no output schema, the description covers all necessary information: what the tool returns and why it should be used. No gaps.

    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, and schema coverage is 100%. The description adds meaning by explaining what the output includes (keys, categories, weights), fulfilling the need for parameter semantics baseline.

    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 that the tool lists all available SEO analyzers with keys, categories, and weights. It distinguishes from sibling tools (analyze, get_stored_analysis, scan_domain) by its specific purpose of discovery before analysis.

    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?

    The description explicitly advises to call this first to discover available analyzers and categories before running an analysis. This provides clear usage context and when to use the tool.

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

GitHub Badge

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.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

octoboost-mcp-server MCP server

Copy to your README.md:

Score Badge

octoboost-mcp-server MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/eugenregehr/octoboost-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server