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ahmedak

defluff

by ahmedak
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Python
Local

Server Quality Checklist

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

  • Latest release: v0.1.2

  • Disambiguation5/5

    Each tool targets a distinct operation: detection, addition to lexicon, and ignoring phrases. No overlap in functionality.

    Naming Consistency5/5

    All tools follow the consistent 'slop_verb' pattern (slop_add, slop_detect, slop_ignore), making names predictable and clear.

    Tool Count5/5

    With 3 tools, the server covers the core operations for a slop detection lexicon (add, detect, ignore). It is well-scoped and not excessive.

    Completeness4/5

    The set covers detection, addition, and whitelisting, but misses removal of phrases from the lexicon and listing the current lexicon, which are minor gaps.

  • Average 4.1/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 6 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 failing

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

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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

  • Behavior3/5

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

    No annotations, so description carries full burden. It discloses that addition flags future detections and explains scope persistence. However, it does not detail side effects like overwriting existing patterns, limits, or reversibility.

    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, each adding value. First states purpose, second gives usage guidance and parameter detail. No redundancy.

    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?

    For a simple tool with 3 params and no output schema, the description covers purpose, when-to-use, and scope. Lacks details on pattern format or category, but adequate for basic use.

    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 coverage is 0%, but description adds meaning for scope (project vs user). Pattern and category are under-explained. Baseline 3 for minimal compensation.

    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 'Add' and resource 'slop lexicon', with the goal of flagging future detections. It distinguishes from siblings 'slop_detect' and 'slop_ignore' by indicating this tool adds rather than detects or ignores.

    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?

    Explicitly says 'Use when the user says something is slop / a banned phrase' and explains scope options. Does not specify when not to use, but the purpose is clear enough to avoid misuse.

    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?

    No annotations are provided, so the description carries the full burden. It discloses the effect (stop flagging) and scope options, but doesn't detail persistence, reversibility, or what happens to existing flags. Adequate but not rich.

    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?

    Three sentences, front-loaded with the core purpose, no wasted words. Every sentence adds value.

    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?

    The description covers the core functionality and scope, but there is no output schema and no mention of return values or side effects. For a simple tool, this is acceptable but not complete.

    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 coverage is 0%, but the description explains 'pattern' as the phrase to ignore and 'scope' as project or user with a default. This adds meaningful context beyond the schema's bare parameter names and types, though it could specify if pattern supports regex.

    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 marks a phrase as NOT slop, distinguishing it from siblings slop_add (adds slop) and slop_detect (detects slop). The verb 'Mark' and resource 'phrase' are specific, and the purpose is unambiguous.

    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 explicitly says 'Use when the user says a flagged phrase is actually fine in this context', providing clear usage guidance. It also explains the scope parameter. It lacks explicit when-not-to-use or direct comparison with siblings, but the context is clear enough.

    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?

    No annotations provided, so description carries full burden. Discloses key traits: deterministic, local, no LLM. Does not mention performance or constraints, but these are reasonable omissions given simplicity.

    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?

    Three sentences front-loaded with purpose, then returns, then use cases. No wasted words; every sentence adds value.

    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?

    Covers purpose, return format, behavior, and use case. Lacks output schema, but description mentions return values (slop_score + spans) sufficiently. Complete for a simple detection tool.

    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?

    Only one parameter 'text' with no schema description (0% coverage). Description adds minimal context: text to detect slop in. Could specify format or max length, but adequate for simple use.

    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 clearly states verb 'Detect' and resource 'AI-slop in text'. It also distinguishes from siblings (slop_add, slop_ignore) by being a detection tool rather than modification or ignoring.

    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 explicit use cases: 'before returning generated prose to self-check, or to gate/rank drafts.' Lacks explicit exclusion of when not to use, but context is clear.

    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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