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sandcastlelabs

collimer-mcp

by sandcastlelabs
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TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v0.2.1

  • Disambiguation1/5

    Both tools have identical descriptions and perform the same function; one is explicitly a deprecated alias. An agent cannot distinguish between them, leading to high ambiguity.

    Naming Consistency2/5

    The names 'beacon_free_scan' and 'collimer_scan' follow different patterns: one uses 'beacon_free_' with an underscore, the other uses the server name directly. The inconsistency is moderate.

    Tool Count2/5

    With only 2 tools and one deprecated, the server feels too sparse for a meaningful interface. It barely covers its intended purpose.

    Completeness2/5

    The server offers only a single scan operation with no additional tools for account management, history, or detailed results, leaving significant gaps for typical usage.

  • Average 4.3/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 12 commits in the last 12 weeks
    • No stable releases found
    • 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.

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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

  • Behavior4/5

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

    Annotations indicate readOnlyHint=true and openWorldHint=true; description adds context about free scan, return values, account unlocking, and re-running for deltas. No contradictions.

    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?

    Description is three sentences plus tip, front-loaded with purpose. Efficient but first sentence is slightly long.

    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 2 parameters and no output schema, description explains return values (score, confidence interval, gap, report URL) and mentions tip about re-running. Fully covers what agent needs.

    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?

    Both parameters (domain, email) have descriptions in schema. Description adds no extra meaning beyond what's already in schema. Schema coverage is 100%, baseline score of 3.

    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?

    Description clearly states it runs a scan on a website to measure AI visibility, listing specific AI models and return values. It distinguishes from sibling (beacon_free_scan) by name but does not explicitly differentiate functionality.

    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?

    Describes when to use (to measure AI visibility) and includes a tip to re-run after changes. However, no explicit guidance on when not to use or comparison with sibling tool.

    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?

    Annotations declare readOnlyHint and openWorldHint. Description adds that the scan is free, returns specific data, and that full report requires a free account. No contradictions with annotations.

    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?

    Four sentences, all informative. Front-loaded with main purpose. No fluff or redundant text.

    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?

    No output schema, but description details what is returned (score, confidence interval, gap, report URL) and explains the limitation of the free scan. Adequate for a 2-parameter tool.

    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?

    Schema description coverage is 100%. Description adds value by explaining 'email' is optional and speeds account claiming, and gives example formats for 'domain' (e.g., 'example.com' or 'https://example.com').

    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?

    Description states it runs a free Collimer scan to measure AI-visibility, specifies the engines (ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews), and lists returned items (score, confidence interval, gap, report URL). Clearly distinguishes from sibling 'collimer_scan' as a deprecated alias.

    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?

    Provides context on when to use (measuring brand visibility) and a tip to re-run after changes. Mentions deprecated alias to differentiate from sibling. Does not explicitly state when not to use or provide alternative tools.

    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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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.

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