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evtapps

Ditto MCP Server

by evtapps
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TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: execute_dql is for running database queries, while ping is a health check. There is no overlap or ambiguity between these functions.

    Naming Consistency4/5

    Both tools follow a verb_noun pattern (execute_dql, ping), though ping is a single word rather than a compound. The naming is consistent in style and readable, with only a minor deviation for the simpler ping.

    Tool Count2/5

    With only 2 tools, the server feels thin for a database query server. While execute_dql is core, the lack of additional tools for operations like schema inspection, data manipulation, or connection management suggests an incomplete surface.

    Completeness2/5

    For a Ditto database server, the tool surface is severely incomplete. It only provides query execution and a health check, missing essential operations such as listing tables, describing schemas, inserting/updating data, or managing transactions, which will limit agent capabilities.

  • Average 3.1/5 across 2 of 2 tools scored.

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

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

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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 full burden for behavioral disclosure. 'Runs a query' implies execution but doesn't specify whether this is read-only, can modify data, requires authentication, has rate limits, or what happens on failure. The description lacks critical behavioral context that should be provided when annotations are absent.

    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 communicates the core purpose without unnecessary words. It's appropriately sized for a tool with comprehensive schema documentation and gets straight to the point with zero wasted text.

    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?

    For a query execution tool with 6 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what DQL is, what types of operations it supports, what the expected return format is, or any behavioral characteristics. The context signals indicate significant complexity that isn't addressed in the description.

    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 documents all 6 parameters thoroughly. The description adds minimal value beyond what's in the schema - it mentions 'parameterized DQL statement' which hints at the 'args' parameter, but doesn't provide additional semantic context about parameter relationships or usage patterns.

    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 ('Runs a query') and the target system ('in Ditto'), with the specific query type ('parameterized DQL statement'). It distinguishes from the only sibling 'ping' by focusing on query execution rather than connectivity testing. However, it doesn't specify what kind of queries (read vs write) or the resource being queried.

    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. With only one sibling ('ping'), it doesn't explain that 'ping' is for connectivity testing while this is for data operations. There's no mention of prerequisites, error conditions, or typical use cases for DQL queries.

    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 carries the full burden. It states this is a 'Health check' which implies a read-only, non-destructive operation, but it doesn't disclose behavioral traits like what 'health' entails (e.g., response time, status codes), whether it requires authentication, or any rate limits. The description is too vague for a tool with no annotation coverage.

    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 any fluff. It is appropriately sized and front-loaded, making it easy to understand at a glance.

    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 simplicity (0 parameters, no annotations, no output schema), the description is minimally adequate. It explains what the tool does but lacks details on behavioral aspects like return values or error conditions. For a health check tool, more context on expected outcomes would improve completeness.

    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 schema description coverage is 100% (though trivial since there are no parameters). The description doesn't need to add parameter semantics, so it meets the baseline of 4 for zero-parameter tools by not introducing unnecessary complexity.

    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 as a 'Health check for the Ditto MCP server' with a specific verb ('check') and resource ('Ditto MCP server'). It distinguishes from the sibling tool 'execute_dql' by focusing on server health rather than data querying. However, it doesn't explicitly contrast with the sibling, so it's not a perfect 5.

    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 typical use cases (e.g., verifying server availability before operations) or exclusions. The context is implied as a health check, but explicit usage instructions are missing.

    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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