MCP MySQL Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: add_column modifies table structure, connect_db handles connections, create_table creates tables, describe_table inspects structure, execute handles write queries, list_tables enumerates tables, and query handles read queries. The separation between execute (for INSERT/UPDATE/DELETE) and query (for SELECT) is particularly well-defined.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with clear, descriptive names in snake_case: add_column, connect_db, create_table, describe_table, execute, list_tables, query. The naming convention is uniform throughout the set, making it easy to predict tool functions.
Tool Count5/5
With 7 tools, this server is well-scoped for MySQL database operations. Each tool serves a distinct, essential function in the database management workflow, from connection and table listing to query execution and schema modification. The count is neither too sparse nor bloated.
Completeness4/5
The tool set covers core MySQL operations effectively: connection management, table CRUD (create, list, describe), schema modification (add_column), and query execution (both read and write). A minor gap is the lack of tools for deleting tables or columns, which might require workarounds using execute, but the surface is otherwise complete for basic database interactions.
Average 2.9/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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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 the full burden of behavioral disclosure. It states the tool adds a column, implying a write/mutation operation, but fails to mention critical aspects like required permissions, whether the change is reversible, potential side effects on existing data, or error conditions. This leaves significant gaps for a mutation tool.
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 with zero wasted words. It's front-loaded with the core purpose and appropriately sized for the tool's complexity, making it easy to parse quickly.
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 mutation tool with no annotations, 0% schema description coverage, no output schema, and nested parameters, the description is inadequate. It lacks details on behavior, parameter usage, return values, and error handling, leaving the agent with insufficient context to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, meaning parameters are undocumented in the schema. The description mentions 'table' and 'field' but provides no details about their meaning, required formats, or constraints (e.g., valid 'type' values, 'nullable' implications). It adds minimal value beyond what's inferred from parameter names.
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 ('Add') and target ('new column to existing table'), providing a specific verb+resource combination. However, it doesn't distinguish this tool from potential alternatives like 'create_table' or 'execute' for similar operations, which prevents a perfect score.
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 like 'create_table' (for new tables) or 'execute' (for SQL commands). There's no mention of prerequisites, such as needing an existing table, or exclusions for when other tools might be more appropriate.
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?
With no annotations provided, the description carries full burden for behavioral disclosure. It states this is a creation operation but doesn't mention whether it requires specific permissions, what happens if the table already exists, whether it's transactional/reversible, or any rate limits. For a mutation tool with zero annotation coverage, this represents a significant gap in behavioral transparency.
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 gets straight to the point with zero wasted words. It's appropriately sized for a basic tool description and front-loads the essential information.
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 database mutation tool with 3 parameters, 33% schema coverage, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns, error conditions, dependencies on other tools (like 'connect_db'), or how parameters interact. The agent would struggle to use this tool effectively without additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 33% (only the 'table' parameter has a description), leaving 'fields' and 'indexes' undocumented in the schema. The description adds no parameter information beyond what's implied by the tool name, failing to compensate for the low schema coverage. The agent must infer parameter meanings from the schema structure alone.
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 ('Create') and resource ('new table in the database'), making the tool's purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'add_column' or 'execute' which might also involve table creation operations, so it doesn't achieve full sibling differentiation.
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 like 'add_column' for modifying existing tables or 'execute' for running SQL commands. There's no mention of prerequisites (e.g., database connection) or typical use cases, leaving the agent with minimal contextual direction.
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 of behavioral disclosure. It states the tool connects to a database but doesn't describe what happens after connection (e.g., whether it returns a connection object, sets a default session, or requires subsequent tools to reference this connection). It also omits critical details like authentication requirements, error handling, timeout behavior, or whether connections are persistent. For a tool with potential security and state implications, this is inadequate.
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 front-loads the core purpose ('Connect to MySQL database') and adds necessary detail ('using URL or config'). There is no wasted language, repetition, or unnecessary elaboration, making it easy to parse quickly.
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?
Given the complexity of a database connection tool with 7 parameters, no annotations, and no output schema, the description is incomplete. It doesn't address what the tool returns (e.g., a connection handle, success status), how errors are handled, or the implications of the connection (e.g., whether it's shared across tools). For a foundational tool in a database server, this leaves too many unknowns for effective agent 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?
The description mentions 'using URL or config,' which loosely maps to the parameters (e.g., 'url' for URL-based connection and other parameters for config-based). However, with 7 parameters and only 43% schema description coverage, the description doesn't compensate for the undocumented parameters like 'host,' 'user,' 'password,' and 'database.' It also fails to explain the relationship between parameters (e.g., if 'url' is provided, are other parameters ignored?) or the 'workspace' parameter's purpose.
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 ('Connect to MySQL database') and the resource ('MySQL database'), with the method ('using URL or config') providing additional specificity. It distinguishes from siblings like 'execute' or 'query' by focusing on establishing a connection rather than performing operations. However, it doesn't explicitly differentiate from potential connection-related tools that might exist in other contexts.
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 prerequisites (e.g., whether this must be called before other database operations), nor does it clarify if it's for initial setup, reconnection, or switching contexts. With siblings like 'execute' and 'query' that likely require an active connection, the lack of usage context is a significant gap.
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?
With no annotations provided, the description carries full burden for behavioral disclosure. 'Get table structure' implies a read-only operation but doesn't specify what 'structure' includes (columns, types, constraints), whether authentication is needed, or how errors are handled. It lacks details about return format or any operational constraints.
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 extremely concise at just three words, with zero wasted language. It's front-loaded with the core purpose and doesn't include any unnecessary elaboration.
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 tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what 'table structure' means in practice, what format the information returns in, or how this differs from related tools. The agent would need to guess about the tool's behavior and output.
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?
The input schema has 100% description coverage, with the 'table' parameter clearly documented as 'Table name'. The description doesn't add any meaningful parameter semantics beyond what the schema already provides, so it meets the baseline for high schema coverage.
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 'Get table structure' clearly states the verb ('Get') and resource ('table structure'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'list_tables' or 'query', which might also provide table information in different ways.
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 like 'list_tables' (which might list table names) or 'query' (which might retrieve table data). There's no mention of prerequisites, context, or exclusions for usage.
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 must fully disclose behavioral traits. It states the tool executes queries but fails to mention critical details like whether it requires authentication, what happens on success/failure (e.g., returns affected rows or error messages), or potential side effects (e.g., data modification). This leaves significant gaps in understanding the tool's behavior.
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, direct sentence that efficiently conveys the core action without unnecessary words. It is front-loaded with the essential information ('Execute an INSERT, UPDATE, or DELETE query'), making it highly concise and well-structured for quick comprehension.
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?
Given the complexity of a database mutation tool with no annotations and no output schema, the description is insufficient. It lacks details on behavioral aspects (e.g., error handling, return values), usage context (e.g., when to use vs. siblings), and does not explain what happens after execution. For a tool that modifies data, this leaves critical gaps in understanding.
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?
The input schema has 100% description coverage, clearly documenting both parameters ('sql' and optional 'params'). The description adds no additional semantic information beyond what the schema provides, such as query format examples or parameter binding specifics. With high schema coverage, the baseline score of 3 is appropriate, as the description does not compensate but also does not detract.
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 verb 'execute' and specifies the resource as 'INSERT, UPDATE, or DELETE query', making the purpose explicit. However, it does not differentiate from sibling tools like 'query' (which likely handles SELECT queries), leaving room for improvement in distinguishing its specific scope.
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 does not mention when to use 'execute' over 'query' (for SELECT) or other siblings like 'create_table', nor does it specify prerequisites such as database connection requirements. This lack of context leaves the agent without clear usage instructions.
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 of behavioral disclosure. 'Execute a SELECT query' implies a read-only operation, but it doesn't clarify permissions required, potential side effects (e.g., locking), rate limits, or error handling. For a tool with database access and no annotations, this leaves critical behavioral traits unspecified.
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 extremely concise—a single, front-loaded sentence that directly states the tool's function without unnecessary words. Every word earns its place, making it easy to parse quickly. There is no wasted verbiage or structural complexity.
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?
Given the complexity of database queries and the absence of both annotations and an output schema, the description is incomplete. It doesn't address return values (e.g., result format, pagination), error conditions, or security considerations (e.g., SQL injection risks). For a tool with significant behavioral implications, more context is needed to guide safe and effective 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 description coverage is 100%, with clear documentation for both parameters ('sql' and optional 'params'). The description adds no additional parameter semantics beyond what the schema provides, such as SQL dialect support or placeholder syntax details. However, with high schema coverage, a baseline score of 3 is appropriate as the schema adequately covers parameter meaning.
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 'Execute a SELECT query' clearly states the action (execute) and resource (SELECT query), making the purpose immediately understandable. It distinguishes from siblings like 'execute' (which might handle non-SELECT queries) and 'describe_table' (metadata operations). However, it doesn't explicitly contrast with all siblings, such as 'add_column' or 'create_table', which are DDL operations.
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 when to choose 'query' over 'execute' (if 'execute' handles other query types), nor does it specify prerequisites like needing an established database connection. Without such context, users must infer usage from the tool name alone.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool lists tables but doesn't mention any behavioral traits like whether it requires database permissions, how results are formatted (e.g., sorted, paginated), or potential errors. This leaves significant gaps in understanding the tool's operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that directly states the tool's purpose without any wasted words. It's front-loaded and efficiently communicates the essential information, making it highly concise and well-structured.
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 output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavioral aspects like result format or usage context. For a read-only list operation, this is acceptable but leaves room for improvement in 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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, aligning with the schema's completeness. A baseline of 4 is applied since no parameters exist, and the description doesn't add unnecessary details.
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 ('List') and resource ('all tables in the database'), making the tool's purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'describe_table' or 'query', which could also involve table information, so it doesn't reach the highest score.
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 such as 'describe_table' (for details on a specific table) or 'query' (for executing SQL queries). It lacks explicit context or exclusions, leaving usage decisions ambiguous.
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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