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EngIcaro

qlik-lineage-mcp

by EngIcaro
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: ghost_files identifies unused data files, while unused_columns identifies unused columns within a file. No overlap.

    Naming Consistency5/5

    Both tool names follow a consistent snake_case verb_noun pattern (ghost_files, unused_columns), making them predictable.

    Tool Count4/5

    With only 2 tools, the server is focused on a narrow domain (identifying unused resources). While minimal, the scope is appropriately scoped for its specific purpose.

    Completeness3/5

    The tools cover file-level and column-level unused resources, but missing capabilities such as listing spaces, retrieving lineage graphs, or identifying unused apps, which may be needed for full lineage analysis.

  • Average 3.4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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 status not available

  • 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

  • Behavior2/5

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

    With no annotations, the description should fully disclose behavioral traits. It does not mention read-only nature, side effects, permissions, or error handling (e.g., space not found).

    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 extremely concise, uses a structured Args format, and front-loads the main purpose. Every sentence adds value without redundancy.

    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's simplicity and lack of output schema, the description should explain return values (e.g., list of file names). Current description only states what it returns but not the format or examples, leaving gaps.

    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 coverage is 0%, so the description must add value. It states that space_name is the display name and case-insensitive, which helps. However, it does not specify expected format or constraints beyond that.

    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?

    The description clearly states the action (return) and resource (data files) with a specific context (space with no app consumption). It implicitly distinguishes from sibling 'unused_columns', but could be more precise about 'data files'.

    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 guidance on when to use this tool versus alternatives like 'unused_columns'. The description only explains what it does without providing usage context or exclusions.

    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 discloses case-insensitivity and staleness detection behavior. However, it does not mention permissions, error handling, or side effects. The description adds some behavioral context beyond the schema but is not fully comprehensive.

    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 concise, uses a clear docstring structure with bulleted Args, and front-loads the core purpose. One sentence could be slightly more direct, but overall efficient.

    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 parameter semantics and some behavioral context, but lacks information about the output format (e.g., list of column names), pagination, or error cases. Given no output schema, this is a notable gap for a query 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 coverage is 0%, so the description carries the full burden. It explains each parameter's purpose, case-insensitivity, format (e.g., 'Sales.qvd'), and the optional lineage_activation_date's effect on staleness detection, adding significant meaning beyond the schema types.

    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 returns columns of a data file that no app consumes, using specific verb-resource combination. This is distinct from the sibling tool 'ghost_files', which likely deals with unused files, not columns.

    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 for finding unused columns and explains parameter constraints, but does not explicitly state when to use this tool versus the alternative 'ghost_files' or provide when-not-to-use guidance.

    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 there are no obvious security issues.
  • Evaluate tool definition quality.

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