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robot-resources

Robot Resources Scraper

by robot-resources
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.2

  • Disambiguation5/5

    The two tools have clearly distinct purposes: scraper_compress_url handles single-page compression, while scraper_crawl_url performs multi-page crawling with BFS link discovery. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (scraper_compress_url and scraper_crawl_url), using the same prefix and snake_case style throughout. The naming is predictable and uniform.

    Tool Count2/5

    With only two tools, the server feels thin for a scraper domain, lacking essential operations like error handling, configuration, or content filtering. This minimal set may limit agent workflows and cause dead ends.

    Completeness2/5

    The tool surface is severely incomplete for web scraping; it misses basic CRUD-like operations such as updating crawl parameters, deleting cached data, or retrieving specific page elements. Agents will struggle with incomplete coverage.

  • Average 3.6/5 across 2 of 2 tools scored.

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

  • This repository is archived. Archived repositories automatically receive an F maintenance tier.

  • 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.

  • 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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the token reduction benefit but fails to disclose critical traits like rate limits, authentication needs, error handling, or what happens with invalid URLs. The description is minimal and does not compensate for the lack of annotations, leaving significant gaps in understanding 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?

    The description is highly concise and front-loaded, consisting of two sentences that directly state the tool's function and outcome without any fluff. Every sentence earns its place by delivering essential information efficiently, making it easy to grasp the tool's purpose quickly.

    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 a tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral aspects, error cases, and output format beyond 'markdown,' failing to provide sufficient context for safe and effective use. The description does not compensate for the absence of structured data, leaving key operational questions unanswered.

    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, providing clear details for all parameters (url, mode, timeout, maxRetries). The description adds no additional parameter semantics beyond what the schema already explains, such as usage examples or implications of mode choices. Thus, it meets the baseline score of 3, as the schema adequately covers parameter information.

    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 with a specific verb ('compress') and resource ('web content from a URL'), and distinguishes it from its sibling 'scraper_crawl_url' by focusing on token reduction rather than crawling. It explicitly mentions the outcome: 'Returns markdown with 70-90% fewer tokens than raw HTML.'

    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 'scraper_crawl_url' or any alternatives. It lacks context about prerequisites, such as when compression is beneficial or when to choose specific modes, offering only a basic functional statement without comparative or exclusionary advice.

    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 carries full burden and does well by disclosing key behavioral traits: the BFS crawling algorithm, multi-page scope, and the 70-90% token reduction in output. It doesn't mention rate limits, authentication needs, or error handling, but provides substantial operational context.

    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 that efficiently convey the core functionality and key output characteristic. Every word earns its place with no redundancy or unnecessary elaboration. The description is front-loaded with the primary action and scope.

    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?

    For a 7-parameter tool with no annotations and no output schema, the description provides good operational context but lacks details about return format structure, error conditions, or performance characteristics. It mentions compressed markdown output but doesn't describe the data structure or pagination approach.

    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 adds no parameter-specific information beyond what's already in the schema descriptions. It mentions 'starting URL' which is covered by the url parameter description, but doesn't elaborate on parameter interactions or usage patterns.

    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 with specific verbs ('crawl multiple pages', 'returns compressed markdown') and resources ('starting URL', 'BFS link discovery'). It distinguishes from the sibling tool scraper_compress_url by emphasizing multi-page crawling rather than single-page compression.

    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 context through 'crawl multiple pages' and 'starting URL', suggesting this is for web scraping tasks. However, it doesn't explicitly state when to use this tool versus the sibling scraper_compress_url or other alternatives, nor does it provide exclusion criteria.

    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 the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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