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jordanmatusik24

fde-week3-agent

by jordanmatusik24
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Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a distinct, non-overlapping purpose: listing tables, describing schema, executing SQL queries, and summarizing results. No two tools could be confused for the same task.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern in snake_case (list_tables, describe_table, query_sql, summarize_results), making the set predictable and easy to use.

    Tool Count5/5

    4 tools perfectly cover the essential workflow for a read-only database agent: discover tables, inspect schema, run queries, and summarize results. Each tool earns its place with no unnecessary additions.

    Completeness4/5

    The set covers the core workflow well, but lacks a tool for quickly previewing sample data or obtaining table statistics, which would require writing a query manually. Minor gap, but functional.

  • Average 4.8/5 across 4 of 4 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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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  • 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

  • Behavior3/5

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

    Annotations already declare readOnlyHint=true. Description does not contradict and adds some context (input types from query_sql), but does not elaborate on behavioral aspects like output length or processing limits.

    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?

    Two concise paragraphs covering purpose, usage guidelines, and parameter explanations. No redundant sentences.

    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?

    Explains purpose, when to use, and parameters. No output schema, but the output is natural language text; minor gap: does not specify output format or length constraints.

    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?

    Schema coverage is 0% but description gives meaningful explanations for both parameters: 'rows' is from query_sql results, 'question' is the user's original query. Fully compensates for missing schema descriptions.

    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 the tool produces a natural-language summary of query results. It distinguishes from sibling tools by specifying it's for narrative interpretation, not single-row lookups.

    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 states when to use (multiple rows, aggregate patterns) and when not to (single-row lookups). Provides clear criteria and alternatives (answer directly).

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

  • Behavior4/5

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

    Annotations already provide readOnlyHint and idempotentHint. The description adds value by specifying the return format (JSON with 'tables' key), the result content (list of names), and the cost profile (cheap to call). No contradiction.

    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?

    Three concise sentences: purpose, usage guidance, technical detail. Every sentence adds value with no redundancy.

    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?

    No output schema exists, but the description adequately explains the return format (JSON with 'tables' key). It also recommends next steps (use describe_table). Complete for a simple list 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?

    No parameters exist, so schema coverage is 100%. Description adds no param details, which is appropriate. Baseline 4 for zero-param tools.

    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 all tables in the Chinook database. It uses a specific verb ('List') and resource ('tables'), and distinguishes from siblings by recommending initial use before describe_table.

    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 says 'Call this FIRST when you don't yet know what data is available' and 'use it freely rather than guessing table names', providing clear when-to-use and when-not-to.

    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?

    Annotations already declare readOnlyHint and idempotentHint. Description adds: parallel calls possible, error on nonexistent table, case-sensitive name. No contradiction.

    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?

    Two short paragraphs plus a bullet for args. Front-loaded with purpose. Every sentence is informative without fluff.

    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?

    Covers usage, error handling, and parameter semantics. Lacks explicit description of the return format (column schema details), but 'column schema' is generally understood for this standard 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?

    Schema has 0% coverage. Description fully compensates by explaining the 'name' parameter: exact, case-sensitive, as returned by list_tables.

    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?

    Clearly states 'Return the column schema for a table' with specific verb and resource. Distinguishes from siblings: list_tables lists tables, query_sql runs queries, summarize_results summarizes – this tool is for schema introspection.

    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 says 'Call this before writing any SQL that references a table you haven't described yet' and suggests using list_tables for valid names. Also mentions parallel calls in one turn.

    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?

    Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds valuable context: it explicitly rejects INSERT/UPDATE/DELETE/DROP, rejects multiple statements, and documents the truncation behavior (max 100 rows). This enriches the agent's understanding beyond annotations.

    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?

    Well-structured with clear sections and bullet points. Front-loaded with purpose. Slightly verbose but every sentence adds essential information. No redundancy.

    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 no output schema, the description thoroughly explains the return format (columns, row_count, rows, truncated). It also covers error cases (rejection of non-SELECT). For a read-only SQL tool, this is fully complete.

    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?

    With 0% schema description coverage, the description fully compensates by exhaustively detailing the sql parameter: it must be a single SELECT statement, standard SQL allowed constructs (JOIN, WHERE, etc.), and explicitly forbids a trailing semicolon. This leaves no ambiguity.

    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 executes a single SELECT statement and returns results, using specific verbs and resources. It distinguishes itself from siblings (list_tables, describe_table, summarize_results) by focusing exclusively on SELECT queries.

    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 states when to use (SELECT only) and when not to use (non-SELECT, multiple statements). Provides clear guidance to first explore schema using list_tables and describe_table, preventing common errors.

    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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