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scriptstar

Data Engineering Tutor MCP Server

by scriptstar
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TypeScript
Hybrid

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: fetching external updates, reading internal memory, and writing to internal memory. There is no overlap in functionality, making it easy for an agent to select the correct tool without confusion.

    Naming Consistency5/5

    All tools follow a consistent 'de_tutor_verb_noun' pattern with snake_case, using descriptive verbs like 'get', 'read', and 'write'. This predictability enhances usability and reduces cognitive load for agents.

    Tool Count3/5

    With only 3 tools, the set feels thin for a tutoring server, as it lacks interactive or instructional tools (e.g., explain concepts, quiz, or provide feedback). While the tools cover basic memory and update operations, the scope seems limited for effective tutoring.

    Completeness2/5

    The toolset is severely incomplete for a Data Engineering Tutor. It includes memory management and update fetching but misses core tutoring functions like explaining concepts, answering questions, or assessing knowledge. This will likely cause agent failures in delivering comprehensive tutoring.

  • Average 3.1/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 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

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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 states the tool updates memory, implying a mutation, but doesn't cover critical aspects like permissions needed, whether changes are reversible, rate limits, or what the response entails. This is a significant gap for a mutation tool without annotation support.

    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, efficient sentence that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, making it easy to understand quickly.

    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 complexity as a mutation tool with no annotations and no output schema, the description is incomplete. It fails to address behavioral traits, usage context, or output expectations, leaving the agent with insufficient information to operate the tool effectively beyond basic parameter input.

    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 description coverage is 100%, meaning the input schema fully documents both parameters ('concept' and 'known'). The description adds no additional semantic details beyond what the schema provides, such as examples or usage context for the parameters, so it meets the baseline for high schema coverage.

    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 ('Updates') and resource ('user's Data Engineering knowledge memory for a specific concept'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'de_tutor_read_memory' or 'de_tutor_get_updates', which likely have different functions (reading vs. writing).

    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?

    The description provides no guidance on when to use this tool versus alternatives, such as the sibling tools mentioned. It lacks context about prerequisites, scenarios for updating memory, or any exclusions, leaving the agent with minimal usage direction.

    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 provided, the description carries the full burden of behavioral disclosure. It mentions the data source ('Perplexity Sonar via OpenRouter') but doesn't describe what 'recent' means (timeframe), whether there are rate limits, authentication requirements, error conditions, or what format the news/updates will be returned in. For a tool with external API dependencies, this is insufficient behavioral context.

    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 efficiently conveys the core purpose and implementation method. Every word earns its place with no redundancy or unnecessary elaboration. It's appropriately sized for a simple, parameterless tool.

    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 external API dependency and lack of annotations or output schema, the description is incomplete. It doesn't explain what 'recent' means, how many items are returned, the format/structure of returned data, error handling, or any limitations. For a tool fetching dynamic external data, this leaves significant gaps for the agent.

    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 0 parameters with 100% schema description coverage, so the schema already fully documents the parameter situation. The description appropriately doesn't discuss parameters since none exist. Baseline for 0 parameters is 4, as the description correctly focuses on the tool's purpose rather than non-existent parameters.

    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 purpose with a specific verb ('fetches') and resource ('recent news and updates about Data Engineering concepts, patterns, and technologies'), and mentions the implementation method ('using Perplexity Sonar via OpenRouter'). However, it doesn't explicitly differentiate from its siblings (de_tutor_read_memory and de_tutor_write_memory), which appear to be memory-related tools rather than news-fetching tools.

    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?

    The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, timing considerations, or comparison with sibling tools. The agent must infer usage context solely from the purpose statement.

    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 provided, the description carries the full burden of behavioral disclosure. It states this is a read operation, implying it's non-destructive, but doesn't cover aspects like authentication needs, rate limits, or what the return format looks like (e.g., structured data or raw text). This leaves significant gaps for a tool that interacts with user memory.

    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, efficient sentence that directly states the tool's function without any fluff or unnecessary details. It's front-loaded and every word earns its place, making it highly concise and well-structured.

    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?

    Given the tool's complexity (simple read operation with no parameters) and the lack of annotations and output schema, the description is minimally adequate. It specifies the resource but doesn't provide details on behavior or output, which could be helpful for an agent. However, for a zero-parameter tool, this is acceptable as a baseline.

    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 0 parameters, and the schema description coverage is 100%, so there's no need for parameter documentation in the description. The baseline for this scenario is 4, as the description appropriately avoids redundant information while clearly indicating what resource is being accessed.

    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 ('Reads') and the resource ('user's current Data Engineering knowledge from memory'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from its sibling 'de_tutor_get_updates', which might also retrieve information, so it doesn't achieve the highest score for sibling distinction.

    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?

    The description provides no guidance on when to use this tool versus alternatives like 'de_tutor_get_updates' or 'de_tutor_write_memory'. There's no mention of prerequisites, context, or exclusions, leaving the agent with minimal usage direction.

    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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