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Kedro MCP Server

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Server Quality Checklist

58%
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  • Latest release: v0.1.2

  • Disambiguation5/5

    Each tool clearly targets a distinct area: general usage, notebook conversion, and project migration. There is no overlap in their described purposes, making selection unambiguous.

    Naming Consistency3/5

    Names are readable but follow different conventions: 'kedro_general_instructions' uses a prefix, 'notebook_to_kedro' uses an arrow-style descriptor, and 'project_migration' is a plain noun phrase. The lack of a uniform pattern is noticeable.

    Tool Count4/5

    The server is narrowly scoped to providing instructions, and three tools cover its intended sub-areas. While not excessive, the set is minimal and could benefit from a few more topics to feel more complete.

    Completeness2/5

    The domain is Kedro, yet the tool surface covers only general guidance, notebook conversion, and project migration. Missing are tools for pipeline development, data catalog management, or execution, which are core Kedro workflows. This leaves significant gaps for agents needing practical help.

  • Average 3.1/5 across 3 of 3 tools scored. Lowest: 2.5/5.

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

    • 0 of 1 community issues answered or closed in the last 6 months
    • 0 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 Apache 2.0.

  • This repository includes a README.md file.

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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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It implies a read operation ('return') but does not mention any permissions, potential errors, or whether it provides a static guide or dynamic lookup. The transparency is minimal.

    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, front-loaded sentence with no wasted words. It is appropriately concise for a tool with no parameters, though it could benefit from additional context. The brevity is effective but not exemplary enough for a 5.

    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?

    Despite the low complexity (0 params) and the presence of an output schema (which obviates the need to describe return values), the description fails to provide context about what 'project migration' means, when to use it, or how it relates to sibling tools. This is a significant completeness gap.

    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?

    The input schema has zero parameters, so there is nothing to document. Per the baseline rule for 0-parameter tools, this dimension earns a 4; the description correctly says nothing about parameters and is not misleading.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose2/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description 'Return project migration instructions' essentially restates the tool name with a generic verb and resource. It does not specify what 'project migration' means or how it differs from siblings like notebook_to_kedro, making it a tautology rather than a clear, distinguishing purpose.

    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?

    There is no guidance on when to use this tool versus alternatives. The description only states what it does, with no mention of scenarios, prerequisites, or exclusions, leaving the agent without decision support.

    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?

    With no annotations, the description must carry the full burden of behavioral disclosure. It only says 'Return... instructions,' which does not reveal whether instructions are generic, notebook-specific, or what form they take, though it is not misleading.

    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?

    A single concise sentence that is front-loaded and contains no filler. Every word 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?

    For a simple info-return tool, the description is adequate but lacks detail about the nature of the instructions, how they relate to sibling tools, and what content is covered. The presence of an output schema reduces the need to explain return values, but overall the description is thin.

    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?

    The tool has zero parameters, so there is nothing to explain beyond the schema. Per guidelines, a baseline of 4 is appropriate.

    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 tool's function: returning conversion instructions from Notebook to Kedro. The verb 'Return' combined with the resource 'Notebook→Kedro conversion instructions' makes it distinct from sibling tools like 'kedro_general_instructions' and 'project_migration'.

    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?

    There is no guidance on when to use this tool versus alternatives such as 'kedro_general_instructions' or 'project_migration'. The description is too brief to convey any selection context.

    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?

    With no annotations, the description carries the full burden of behavioral disclosure. It only states that guidance is returned, without detailing what topics are covered, the format of the output, or any limitations. This is minimal and leaves significant ambiguity about the tool's behavior.

    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, succinct sentence with no redundant information. It earns its place by stating the core purpose, exemplifying high conciseness and clear structure.

    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?

    For a parameterless tool, the description is minimally adequate but leaves gaps: it doesn't specify the scope of 'general Kedro usage guidance' or how the output schema relates to the returned content. The presence of an output schema partially compensates, but the description alone is incomplete for full understanding.

    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?

    The tool has zero parameters, so there is nothing to explain about inputs. The baseline for 0 parameters is 4, and the description appropriately does not need to add parameter-level 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's function: 'Return general Kedro usage guidance.' This uses a specific verb ('return') and resource ('general Kedro usage guidance'), distinguishing it from sibling tools like notebook_to_kedro and project_migration which handle specific conversion/migration tasks.

    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 usage context is implied by the tool name and description—it is for general guidance—but there is no explicit instruction on when to use this tool versus alternatives. No exclusions or alternative tools are mentioned, so the guidance is only implicit.

    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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