typescript-mcp-sqlite-mini-lab
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The two tools, db.list_tables and db.describe_table, have clearly distinct purposes with no functional overlap.
Naming Consistency5/5Both tools follow a consistent 'db.' prefix and snake_case naming pattern, making them predictable.
Tool Count2/5With only 2 tools, the server feels under-scoped for a database interface, falling well below the typical 3-15 range.
Completeness1/5The toolset only supports listing tables and describing schemas, missing essential operations like querying or modifying data, leaving the surface severely incomplete.
Average 3.7/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 8 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
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, and the description uses the verb 'describe,' which reasonably implies a read-only operation. However, it does not disclose potential side effects, error conditions, or whether the table must exist, leaving some behavioral aspects unstated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no redundant words or extraneous details, making it highly concise and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple describe operation, the description is adequate but minimal. It does not specify the output format, what fields the schema includes, or how errors like missing tables are handled, which could leave an agent uncertain in edge cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The parameter 'tableName' is self-explanatory by name and constrained to a non-empty string, but the description adds no additional meaning about expected format, case sensitivity, or identifier rules beyond the basic schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('describe') and the resource ('schema of a specific table'), making the primary purpose obvious. It does not explicitly distinguish itself from the sibling tool db.list_tables, but the focus on a specific table rather than all tables provides implicit contrast.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when a schema for one specific table is needed, but it does not explicitly state when to prefer this tool over db.list_tables or provide any contextual guidance or prerequisites.
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, and the description does not disclose any behavioral traits such as read-only status, side effects, or performance implications. While listing is inherently non-destructive, this is not stated explicitly, so transparency is lacking beyond the obvious action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no redundant information. It is optimal in length and structure for the task.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple listing operation with no parameters and no output schema, the description is complete. It tells the agent exactly what the tool does without needing additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema has no properties. Per the rubric, this yields a baseline of 4. The description correctly implies no parameters are needed, and there is nothing to add.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description 'List all tables in the SQLite database' uses a specific verb (List) and names the exact resource (all tables in the SQLite database). It clearly distinguishes this tool from the sibling 'db.describe_table', which focuses on describing a single table.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving the full table list but does not explicitly state when to use this versus the sibling tool, nor does it mention any exclusions or alternatives. The guidance is left to inference.
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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