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gdavos007

Statistical Testing MCP Server

by gdavos007
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Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: listing tables, profiling a table, and running a statistical test. No overlap or ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case: list_tables, profile_table, run_test.

    Tool Count4/5

    3 tools is a reasonable number for a focused statistical testing server, though slightly minimal. No unnecessary tools.

    Completeness3/5

    Covers table discovery, profiling, and two statistical tests, but lacks broader test selection (e.g., ANOVA, chi-squared) and column metadata retrieval.

  • Average 4.5/5 across 3 of 3 tools scored. Lowest: 3.9/5.

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

    • No community issues in the last 6 months
    • 21 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

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  • 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

  • Behavior4/5

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

    No annotations provided, so description carries full burden. It discloses deterministic ordering, hard limit clamping, truncation reporting, null handling, advisory roles without LLM, and PII limitations. This is thorough for a profiling tool, though it omits explicit read-only guarantee.

    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?

    Description is a single paragraph of about 5 sentences covering key aspects without fluff. It is front-loaded with the purpose. Could be more structured (e.g., bullet points), but each 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?

    Given an output schema exists, description does not need to detail return values. It covers constraints (no SQL, no LLM), behavioral traits (null handling, example capping), and limitations (not comprehensive PII detector). Complete enough for typical 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 100%, so baseline is 3. Description adds minimal nuance: 'conservative table identifier' matches schema, and 'server hard limit always applies' is already in schema. No substantial new meaning beyond schema.

    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 states 'Profile one table using deterministic, rule-based heuristics.' This clearly specifies the verb (profile), resource (one table), and method (deterministic, rule-based heuristics), distinguishing it from siblings list_tables and run_test.

    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?

    Description explains tool mechanics and constraints (e.g., 'SQL and sampling cannot be provided', 'examples are capped') but does not explicitly state when to use this tool over siblings like list_tables or run_test. Usage context is implied but not formally contrasted.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior5/5

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

    With no annotations, the description fully discloses all behavioral traits: read-only, no extraction of data, no return of credentials or path, and inapplicability of row-limit/null-handling.

    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?

    Four concise sentences, front-loaded with core purpose, no wasted words.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Despite having no parameters, the description is fully adequate for a simple discovery tool, especially with an output schema present (not shown but referenced).

    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?

    No parameters exist; schema coverage is 100%. The description adds nothing beyond the schema, but that is acceptable for zero parameters. Baseline of 4 applies.

    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 lists tables and views from the SQLite database, using specific verbs and resource. It distinguishes from siblings like profile_table and run_test.

    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 advises use 'before profiling or testing', and clarifies what it does not do (list columns, execute SQL). Does not explicitly state when not to use, but context is sufficient.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior5/5

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

    No annotations provided, so description carries full burden. It discloses that null rows are excluded and counted, Welch rejects non-numeric outcomes, results include warnings and audit metadata, hard row limit applies, and reports truncation/first-N bias.

    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?

    Description is comprehensive but somewhat lengthy (single paragraph). It packs all necessary information, though could be slightly more concise by splitting into sections. No unnecessary repetition.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given output schema exists, description doesn't need return values. It covers assumptions, limitations, warnings, audit metadata, and edge cases (nulls, caps). Complete for a statistical test tool.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Input schema has 100% coverage, but description adds significant meaning beyond schema descriptions. It explains test_id options, required group_values count, explicit success_value for proportion test, and row cap behavior.

    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 it runs an approved statistical test (Welch t-test or two-proportion z-test) on a bounded table extract. It distinguishes from sibling tools (list_tables, profile_table) by specifying its unique purpose of performing hypothesis tests.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    Explicitly provides when to use each test: Welch for two-sided numeric means comparison, two-proportion z-test for binary proportions. Also states when not to use (paired/repeated observations, causal conclusions) and includes conditions like required sample sizes.

    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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