Skip to main content
Glama
davinoishi

Broken Link Checker MCP Server

by davinoishi
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Local

Server Quality Checklist

58%
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: check_page_links targets a single HTML page, while check_site handles an entire website with recursive crawling. There is no overlap in scope or functionality, making it easy for an agent to choose the appropriate tool based on the task.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (check_page_links and check_site), using the same verb 'check' and descriptive nouns that clearly indicate their targets. The naming is predictable and readable throughout.

    Tool Count2/5

    With only two tools, the server feels thin for a broken link checker domain. While the tools cover single-page and site-wide checks, there are likely missing operations such as checking specific links, managing crawl settings, or handling reports, which could limit agent workflows.

    Completeness3/5

    The tools provide basic coverage for checking links on a page or site, but there are notable gaps. For example, there is no tool to check individual links, configure crawl parameters, or retrieve historical results, which might be expected in a more complete link-checking surface.

  • Average 4.1/5 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.

  • 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

  • Behavior3/5

    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 describes the operation (checking links for broken status) and the return format (detailed information about each link), but doesn't mention important behavioral aspects like rate limits, timeout behavior, authentication requirements, or what constitutes a 'broken' link. It adds some value but leaves significant gaps.

    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 perfectly concise with two sentences that each earn their place: the first states the core functionality, and the second describes the return format. There's no wasted language, and the information is front-loaded with the primary purpose.

    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 no annotations and no output schema, the description provides basic operational context but lacks important details about the tool's behavior, error handling, and return format specifics. For a tool that performs network operations and returns complex results, more information about limitations, performance characteristics, and output structure would be beneficial.

    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 description coverage is 100%, so all parameters are well-documented in the schema itself. The description doesn't add any parameter-specific information beyond what's already in the schema descriptions, so it meets the baseline for high schema coverage without providing additional semantic context.

    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 specific action ('check all links on a single HTML page for broken links') and distinguishes it from the sibling tool 'check_site' by specifying it's for a single page rather than an entire site. It includes both the verb ('check') and resource ('links on a single HTML page') with precise scope.

    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 provides clear context about what the tool does (single page link checking), which implicitly differentiates it from the sibling 'check_site' tool that presumably checks an entire site. However, it doesn't explicitly state when to use this tool versus 'check_site' or provide any exclusion criteria.

    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 the full burden of behavioral disclosure. It effectively describes key behavioral traits: the recursive crawling nature, scanning of multiple pages, checking of both internal and external links, and the performance warning about time consumption on large sites. This gives the agent important context about what to expect from this operation.

    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 perfectly concise and well-structured in just two sentences. The first sentence clearly states the tool's purpose and scope, while the second provides important usage guidance. Every word earns its place with no redundancy or unnecessary information.

    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?

    Given the tool's complexity (recursive crawling operation with performance implications), no annotations, and no output schema, the description does a good job of providing essential context. It explains what the tool does, its scope, and important behavioral considerations. However, it doesn't describe what the output looks like or any error conditions, which would be helpful given the absence of an output schema.

    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, so all parameters are well-documented in the schema itself. The description doesn't add any additional parameter semantics beyond what's already in the schema descriptions. The baseline score of 3 is appropriate when the schema does the heavy lifting of parameter documentation.

    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 ('recursively crawl and check all links') and resource ('across an entire website'). It distinguishes from the sibling tool 'check_page_links' by emphasizing it checks 'all links across an entire website' rather than just a single page.

    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 provides clear context for when to use this tool ('Use with caution on large sites as it may take significant time'), which helps the agent understand performance implications. However, it doesn't explicitly state when to use the alternative 'check_page_links' tool versus this one, nor does it provide exclusion criteria beyond the caution about large sites.

    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

broken-link-checker-mcp MCP server

Copy to your README.md:

Score Badge

broken-link-checker-mcp 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/davinoishi/broken-link-checker-mcp'

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