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LareLabs

refinery-mcp

by LareLabs
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v0.1.6

  • Disambiguation5/5

    Each tool targets a distinct input source: raw HTML, URL, or estimation, with no functional overlap.

    Naming Consistency5/5

    All tools use consistent verb_noun snake_case pattern (clean_html, clean_url, estimate_savings).

    Tool Count5/5

    Three tools is ideal for this narrow domain of HTML cleaning and token savings estimation.

    Completeness5/5

    The tool set covers all core operations: cleaning input text, fetching and cleaning a URL, and estimating token savings without a call.

  • Average 3.3/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 14 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.

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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?

    No annotations are provided, so the description must bear the full burden. It omits behavioral traits such as whether fetching is destructive, authentication requirements, rate limits, or error handling (e.g., invalid URL). The mention of 'Refinery Apify actor' is ambiguous.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is concise (one sentence) and front-loaded with the primary action, but it is too brief, sacrificing necessary detail. It could be expanded without becoming verbose.

    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 lack of output schema and low parameter coverage, the description is incomplete. It does not explain the return format, how cleaning is performed, or the role of each parameter. A user cannot fully understand the tool's usage.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is only 33% (only 'url' has a description). The description adds no meaning for 'removeStyles' and 'removeScripts' parameters, leaving their purpose unclear. It does not compensate for the schema's lack of detail.

    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 fetches a URL via the Refinery Apify actor and returns clean LLM-ready text plus word count. This distinguishes it from siblings like clean_html (likely HTML input) and estimate_savings (cost estimation).

    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?

    No usage guidance is provided. The description does not specify when to use this tool versus alternatives like clean_html, nor does it mention prerequisites or scenarios where it should not be used.

    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?

    No annotations are provided, so the description must disclose behavioral traits. It only states 'Clean' and from the parameter description 'strip and normalize.' It does not detail what cleaning entails (e.g., removal of scripts, styles), side effects, or error conditions, leaving 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.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single concise sentence, front-loaded with the key action. It avoids verbosity, though it could include more detail without becoming overly long. Still, it is efficient.

    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 tool has three parameters, no output schema, and low schema coverage, the description is insufficient. It provides no information about return values, default behaviors of boolean parameters, or any preconditions, making it incomplete for an agent to use confidently.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is only 33% (only the 'html' parameter has a description). The description fails to explain the purpose of the 'extractHashtags' and 'extractMentions' boolean parameters, leaving the agent to guess. With such low coverage, the description should compensate but does not.

    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 'Clean raw HTML' with a specific verb and object, and specifies the source (agent, crawler, browser session). It distinguishes this tool from sibling tools like clean_url and estimate_savings by focusing on HTML cleaning.

    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 tells when to use this tool: when you have raw HTML from fetching. It does not provide explicit exclusions or alternatives, but the context of 'already fetched' implies a specific scenario, which is helpful.

    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?

    With no annotations provided, the description must disclose behavioral traits. It states the tool does not make an Apify call, indicating it is a local computation. However, it does not mention any other side effects, required permissions, or return behavior, leaving some 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 a single, well-structured sentence that front-loads the primary purpose. Every word is necessary, and there is no redundant information.

    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?

    The tool has no output schema, so the description should explain the return value (e.g., type of savings, format). It fails to do so, leaving the agent uncertain about what the tool returns. The description is too minimal for a tool with this simplicity level.

    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 defines each parameter. The description adds minimal additional meaning beyond the schema, maintaining a baseline score of 3.

    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 verb 'estimate token savings' and identifies the resource ('raw HTML vs cleaned text'). The phrase 'without making an Apify call' distinguishes it from sibling tools like clean_html and clean_url, which likely involve API calls.

    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 (estimating savings before deciding to clean) but does not provide explicit guidance on when to use this tool versus alternatives, nor does it state any prerequisites or exclusions.

    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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