MCP SQLite Server (Read-Only)
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool has a clearly distinct purpose: listing tables/views, describing schema details, and executing read-only queries. There is no functional overlap or ambiguity between them.
Naming Consistency5/5All tool names follow the same snake_case verb_noun pattern (list_tables, describe_table, query_database), offering a consistent and predictable naming convention.
Tool Count5/5With only 3 tools, the server is well-scoped for a read-only SQLite interface. Each tool covers a distinct and essential operation, and the count is ideal for the purpose.
Completeness5/5For a read-only SQLite server, the toolset is complete: listing tables, describing schema, and querying data with pagination cover all typical use cases. Even edge cases like EXPLAIN and read-only PRAGMAs are supported via query_database.
Average 4.4/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 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
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses that the operation is read-only and details the return structure (JSON with specific keys). It does not mention error handling, permission requirements, or side effects, but for a read-only introspection tool these are minor. The description adds value by describing what information is returned, beyond what annotations would provide.
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, dense sentence that front-loads the primary purpose and then enumerates the exact components and return keys. Every phrase adds information—no filler or redundancy. It is concise yet comprehensive, structuring the behavior clearly.
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?
Given there is no output schema, the description explicitly lists the return keys ('table', 'columns', 'foreign_keys', 'row_count', 'sql') and details column attributes. This fully equips an agent to interpret the result. It also covers the read-only nature and the scope (schema description). For a single-parameter introspection tool, nothing essential is missing.
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?
Schema description coverage is 100% for the single parameter, with the schema saying 'Name of the table to describe.' The description adds no additional meaning beyond that—it doesn't explain how to obtain valid table names (e.g., via list_tables) or any format constraints. Since the schema already fully documents the parameter, the description's contribution is minimal, matching the baseline of 3.
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 states a specific verb ('Describe') and resource ('a table') with clear detail on what is covered: columns with type/notnull/default/PK, foreign keys, row count, and the CREATE statement. It is unambiguous and distinct from siblings like list_tables (which presumably lists table names) and query_database (which executes queries). The purpose is immediately clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly defines when to use it: when you need schema metadata for a specific table. It states it is 'Read-only', which implies it is safe for inspection. However, it does not explicitly contrast with list_tables or query_database, nor mention any exclusions (e.g., when to avoid it). Since the usage context is clear but alternatives are not named, a score of 4 is appropriate.
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?
With no annotations provided, the description carries the burden. It explicitly states 'This is a read-only operation,' disclosing it has no side effects. It also discloses the exclusion of internal tables and the exact return format. This is good behavioral disclosure for a simple list operation.
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?
Two sentences with no fluff. Purpose is front-loaded, return format is given, and the read-only note is appended. Every sentence earns its place.
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 list tool with no params and no output schema, the description fully covers what the agent needs: the scope (user tables/views), the exclusion of internal tables, and the exact JSON return shape. Nothing missing.
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?
There are zero parameters, so the schema is trivially covered at 100%. Per the baseline for 0 params, the description doesn't need to add parameter semantics, and it doesn't. No gaps.
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 clearly states it lists all user tables and views, excluding internal sqlite_* tables. This specific verb+resource combination distinguishes it from siblings like describe_table (specific table) and query_database (run queries).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies when to use it: to get an overview of all tables/views. However, it does not explicitly mention alternatives or when not to use it, but the contrast with siblings is obvious enough. Lacks explicit exclusions.
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 provided, the description carries full responsibility for disclosing behavior, and it does so thoroughly. It states the read-only nature, rejection of destructive statements, pagination behavior (default limit of 100, max 1000, offset support), and signals when more rows exist (truncated flag). The return format is fully specified, which is exceptional given the absence of annotations.
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 three sentences long, front-loaded with the core purpose and restrictions, then pagination, then output format. Every sentence contributes essential information with zero redundancy or fluff. It is structured so the most critical constraints (read-only, rejected statements) appear first.
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 SQL query tool with no output schema and no annotations, the description is remarkably complete. It explains the allowed statements, the rejection rules, pagination mechanics, and the exact JSON response structure. An agent has everything required to call the tool correctly and interpret results. Error handling isn't mentioned, but that is a minor omission given the breadth of what is covered.
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?
Schema description coverage is 100%—all three parameters have descriptive text in the schema. The description adds context around pagination (use limit/offset) but does not introduce new semantic information beyond what the schema already provides. The default limit and max are already in the schema, so the description's added value is limited to reinforcing the pagination workflow.
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 states a specific verb ('Execute') and resource ('read-only SQL query') and enumerates the allowed statement types (SELECT, WITH, EXPLAIN, read-only PRAGMA). It clearly distinguishes itself from sibling tools by focusing on arbitrary query execution rather than metadata listing, so an agent can tell it apart immediately.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description makes clear the tool is for read-only queries and explicitly lists what is rejected (destructive statements, multi-statement, comments). It does not name sibling tools or give explicit 'when to use vs. alternatives' guidance, but the context is unambiguous—if you need to run a SELECT or similar, use this. The exclusion criteria are, however, implied rather than spelled out.
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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