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Crawleo

crawleo-mcp

by Crawleo
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: crawl_web targets a specific webpage for content extraction, while search_web performs broader web searches. There is no overlap in functionality, making it easy for an agent to choose the correct tool based on the need for focused crawling versus exploratory searching.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (crawl_web, search_web) with clear, descriptive names that align with their functions. The naming is uniform and predictable, using snake_case throughout without any deviations.

    Tool Count2/5

    With only two tools, the server feels under-scoped for a web crawling and search domain. While the tools cover basic operations, the lack of additional tools (e.g., for managing crawl sessions, filtering results, or handling errors) limits functionality and may require agents to work around gaps in more complex workflows.

    Completeness2/5

    The tool surface is severely incomplete for a web crawling and search server. There are no tools for operations like configuring crawl parameters, managing search history, handling authentication, or processing batch requests, which are common in such domains. This will likely cause agent failures in extended or nuanced tasks.

  • Average 2.9/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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

  • 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.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

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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

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the extraction of content in various formats but fails to disclose critical behavioral traits such as rate limits, authentication requirements, error handling, or what happens when crawling fails. For a web crawling tool with zero annotation coverage, this is a significant gap in 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 a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part of the sentence contributes directly to understanding the tool's function, making it highly concise and well-structured.

    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 complexity of web crawling (which involves network operations, potential failures, and format handling) and the lack of annotations and output schema, the description is incomplete. It doesn't address error conditions, return formats beyond naming them, or operational constraints, leaving significant gaps for the agent to infer behavior.

    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 schema already documents all four parameters thoroughly. The description adds minimal value beyond the schema by mentioning 'various formats including Markdown, raw HTML, and AI-enhanced HTML,' which loosely corresponds to the boolean parameters but doesn't provide additional semantic context. Baseline 3 is appropriate when the schema does the heavy lifting.

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

    Purpose4/5

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

    The description clearly states the tool's purpose with a specific verb ('crawl') and resource ('webpage'), and specifies the action ('extract its content in various formats'). It distinguishes from the sibling 'search_web' by focusing on content extraction from a specific URL rather than searching. However, it doesn't explicitly contrast with the sibling beyond the different action verbs.

    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 the sibling 'search_web' or other alternatives. It mentions what the tool does but offers no context about appropriate use cases, prerequisites, or exclusions. This leaves the agent without clear direction on 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 provided, the description carries the full burden of behavioral disclosure. It mentions that results include 'optional AI-enhanced HTML, markdown content, and structured data,' which adds some context about output features. However, it fails to disclose critical behavioral traits such as rate limits, authentication needs, costs (implied by 'costs 1 credit' in the schema but not in the description), or error handling. For a search tool with 10 parameters, this is insufficient.

    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 concise and front-loaded, consisting of two sentences that directly state the tool's purpose and key output features. There's no wasted verbiage, and it efficiently communicates the core functionality. A slight deduction is made because it could integrate cost or sibling tool context more seamlessly.

    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?

    Given the tool's complexity (10 parameters, no annotations, no output schema), the description is moderately complete. It covers the basic purpose and output formats but lacks details on behavioral aspects, usage guidelines, and output structure. Without annotations or an output schema, the agent must infer much from the input schema alone, leaving gaps in understanding the tool's full context.

    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 schema fully documents all 10 parameters. The description adds minimal value beyond the schema by hinting at 'AI-enhanced' outputs and 'structured data,' but it doesn't elaborate on parameter interactions or provide additional semantic context. This meets the baseline of 3 when the schema does the heavy lifting.

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

    Purpose4/5

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

    The description clearly states the tool's purpose: 'Search the web using Crawleo's AI-powered search engine.' It specifies the verb ('Search') and resource ('the web'), and mentions the engine's AI-powered nature. However, it doesn't explicitly differentiate from its sibling tool 'crawl_web' (e.g., by contrasting search vs. crawl operations), which prevents a perfect score.

    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 its sibling 'crawl_web' or other alternatives. It lacks context about appropriate scenarios, exclusions, or prerequisites (e.g., when to prefer search over crawl). This leaves the agent without usage direction beyond the basic purpose.

    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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