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keenableai

Keenable MCP

by keenableai
OverviewSchemaRelated ServersScoreDiscussions
JavaScript

Server Quality Checklist

83%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.1

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one fetches content from a specific page, the other searches the web. There is no overlap.

    Naming Consistency5/5

    Both tools use a consistent verb_noun pattern in snake_case: 'fetch_page_content' and 'search_web_pages'.

    Tool Count3/5

    Only 2 tools, which is below the typical 3-15 range, but the domain is narrow (web search and page fetching), so it may be acceptable.

    Completeness4/5

    The set covers the basic workflow of searching and fetching content. Minor gaps exist (e.g., no tool to list search results separately), but the core functionality is complete.

  • Average 4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 13 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.

  • 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.

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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

  • Behavior3/5

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

    Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds that the output is markdown format, which is useful but does not disclose other behaviors like handling of JavaScript, rate limits, or error states.

    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 action and output. Every word adds value with no redundancy.

    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 tool with three parameters and no output schema, the description covers the core function and return format. It lacks detail on error handling or content extraction specifics, but given the simplicity, it is largely complete.

    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% coverage, so the description does not need to add parameter details. It provides no additional meaning beyond the schema, maintaining the baseline score.

    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 action: 'Fetch and extract content from a web page.' It includes the output format ('Returns the page content in markdown format.'), which differentiates it from the sibling tool 'search_web_pages' that presumably searches for pages rather than fetching a specific URL.

    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 that the tool is used when a specific URL is known, in contrast to 'search_web_pages,' but it does not explicitly state when to use or not use this tool, nor does it provide alternative suggestions.

    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?

    Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description adds the context 'live' to indicate real-time results. It does not detail response format or pagination, but the annotations cover the safety profile adequately.

    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 consists of two concise sentences with no wasted words. The key action 'Live web search' is front-loaded, making the purpose immediately clear.

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

    Completeness3/5

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

    While the description effectively conveys the core search behavior, it lacks details about the response (e.g., result count, sorting) and potential limitations. Given the parameter richness (7 params) and no output schema, additional context would improve completeness for an agent.

    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?

    With 100% schema description coverage, the baseline is 3. The description adds value by explaining the intent of the query parameter ('describe the ideal page, not keywords') beyond the schema's 'natural language search query', and it reinforces the use of date/site filters.

    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 'Live web search over the Keenable web index', specifying the verb 'search' and the resource 'web pages'. It distinguishes from the sibling tool 'fetch_page_content' by focusing on discovery rather than retrieval.

    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 advises 'Describe the ideal page, not keywords' and prompts using date/site filters, offering concrete query formulation guidance. However, it does not explicitly state when to use this tool versus the sibling 'fetch_page_content', leaving the differentiation implied.

    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 there are no obvious security issues.
  • Evaluate tool definition quality.

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