PostgreSQL MCP 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: connect/disconnect manage connections, list_tables enumerates tables, describe_table provides table details, get_schema shows relationships, and execute_query runs read-only SQL. An agent can easily distinguish between these functions.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with snake_case naming (e.g., connect_database, describe_table, execute_query). The naming is predictable and readable throughout the set.
Tool Count4/5
With 6 tools, the count is reasonable for a PostgreSQL server focused on schema exploration and read-only queries. It's slightly lean but covers core database interaction needs without bloat.
Completeness3/5
The tools cover connection management, schema inspection, and read-only queries well, but there are notable gaps: no write operations (INSERT, UPDATE, DELETE) or administrative functions (e.g., create/drop tables). This limits the server to read-only use cases, which may cause agent failures for broader database tasks.
Average 3.3/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
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- No high-severity vulnerability alerts
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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, the description carries full burden but only states the basic action. It lacks critical behavioral details: whether this establishes a persistent connection, requires authentication, has side effects (e.g., opening network ports), error handling, or what 'connect' means in context (e.g., returns a connection object).
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 appropriately sized for a tool with a straightforward purpose and well-documented schema, making it easy to parse.
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 connection tool with no annotations and no output schema, the description is inadequate. It doesn't explain what happens after connecting (e.g., state changes, returned handles), error conditions, or dependencies on other tools. Given the complexity of database connections, more context is needed.
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%, so parameters are fully documented in the schema. The description adds no parameter-specific information beyond implying PostgreSQL context, which doesn't enhance understanding of individual parameters. Baseline 3 is appropriate as the schema does the heavy lifting.
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') and resource ('a PostgreSQL database'), making the purpose unambiguous. However, it doesn't differentiate from sibling tools like 'disconnect_database' or explain what 'connect' entails operationally, preventing 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?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a database instance), when to reconnect, or how it relates to siblings like 'execute_query' or 'disconnect_database'.
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 this is a read operation ('Get detailed information'), which is clear, but it doesn't cover aspects like permissions needed, error handling, or what the output format looks like (e.g., structured data or raw text). This leaves gaps for an agent to understand the tool's behavior fully.
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 without any wasted words. It is appropriately sized for a simple tool with one parameter, making it easy for an agent to parse quickly.
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 low complexity (1 parameter, no annotations, no output schema), the description is adequate but incomplete. It covers the basic purpose but lacks details on usage context, behavioral traits, and output expectations, which are needed for full agent understanding in a database environment.
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 single parameter 'tableName' documented as 'Name of the table to describe'. The description adds no additional meaning beyond this, such as format examples or constraints, so it meets the baseline of 3 where the schema does the heavy lifting.
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 ('Get detailed information') and resource ('table's structure, columns, and constraints'), making the purpose specific and understandable. It distinguishes from siblings like 'list_tables' (which lists names) and 'get_schema' (which might cover broader schema info), though it doesn't explicitly name these alternatives.
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 'get_schema' or 'execute_query', nor does it mention prerequisites such as needing an active database connection. It implies usage for table details but lacks explicit context or exclusions.
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. While 'Disconnect' implies a state-changing operation, it doesn't specify whether this is reversible, what cleanup occurs, whether it affects other operations, or what happens on success/failure. The description is too minimal for a tool that likely has important side effects.
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 communicates the essential action without any wasted words. It's perfectly front-loaded with the core purpose and doesn't include unnecessary elaboration. Every word earns its place in this minimal but complete statement.
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 that performs a state-changing operation (disconnecting from a database) with no annotations and no output schema, the description is inadequate. It doesn't explain what 'disconnect' entails, what gets cleaned up, whether this affects subsequent operations, what the response looks like, or error conditions. Given the complexity of database connection management, more context is needed.
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 tool has zero parameters with 100% schema description coverage, so the schema already fully documents the parameter situation. The description appropriately doesn't waste space discussing nonexistent parameters, maintaining focus on the tool's purpose. A baseline of 4 is appropriate for zero-parameter tools.
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 ('Disconnect from') and target resource ('the current PostgreSQL database'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from its siblings like 'connect_database' beyond the obvious opposite action, missing an opportunity to clarify the relationship between connection management tools.
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 or what prerequisites might be needed. It doesn't mention whether a database connection must be active first, what happens if no connection exists, or how this relates to sibling tools like 'connect_database' in a workflow.
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. It states the action but does not disclose behavioral traits such as whether this is a read-only operation, if it requires authentication, potential rate limits, or the format of the returned list. This leaves significant gaps in understanding how the tool behaves.
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 unnecessary words. It is front-loaded and efficiently conveys the essential information, making it easy for an agent 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 lack of annotations and output schema, the description is incomplete. It does not explain what the output looks like (e.g., list format, pagination) or address behavioral aspects like error handling. For a tool with no structured metadata, more context is needed to fully guide the agent.
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 tool has 0 parameters, and the input schema has 100% description coverage (though empty). The description does not need to add parameter details, so it appropriately avoids redundancy. A baseline score of 4 is given since no parameters are present, and the description does not mislead about inputs.
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 ('tables in the current database'), making the purpose immediately understandable. However, it does not explicitly differentiate from sibling tools like 'get_schema' or 'describe_table', which might also provide table-related information, so it falls short of 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 such as 'get_schema' or 'describe_table'. It lacks context about prerequisites (e.g., needing a database connection) or exclusions, leaving the agent to infer usage based on tool names 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes what the tool does ('Get an overview') but lacks details on behavioral traits such as whether it's read-only (implied but not stated), performance characteristics (e.g., if it's slow for large schemas), error handling, or output format. For a tool with zero annotation coverage, this is a significant gap, though it's not misleading—just insufficient.
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 efficiently conveys the tool's purpose without any wasted words. It's front-loaded with the main action ('Get an overview') and specifies the scope ('database schema including tables and their relationships'), making it easy to understand at a glance. Every part of the sentence earns its place, achieving optimal conciseness.
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 complexity (simple read operation with no parameters) and the lack of annotations and output schema, the description is adequate but not complete. It explains what the tool does but doesn't cover behavioral aspects like output format or potential limitations. For a tool in this context, it meets minimum viability but could benefit from more detail to fully guide an agent, scoring a 3.
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 tool has 0 parameters, and schema description coverage is 100% (since there are no parameters to describe). The description doesn't need to add parameter semantics, so it meets the baseline of 4 for tools with no parameters. It appropriately focuses on the tool's purpose without unnecessary parameter 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 tool's purpose with a specific verb ('Get') and resource ('database schema'), specifying it includes 'tables and their relationships'. It distinguishes from siblings like 'list_tables' (which might just list names) and 'describe_table' (which focuses on a single table). However, it doesn't explicitly differentiate from potential alternatives like 'execute_query' for schema queries, keeping it at 4 rather than 5.
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?
The description implies usage when needing an overview of the database structure, which suggests context for database exploration or query planning. However, it doesn't explicitly state when to use this tool versus alternatives like 'list_tables' (for just table names) or 'describe_table' (for detailed info on a specific table), nor does it mention prerequisites such as needing a connected database first. This leaves some ambiguity, scoring a 3 for implied but incomplete guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 clearly states the read-only constraint and acceptable statement types, which is valuable. However, it doesn't mention important behavioral aspects like error handling, timeout behavior, result format, or authentication requirements that would be helpful for an agent.
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 communicates the essential information with zero waste. It's appropriately sized and front-loaded with the core purpose, making it easy for an agent to parse quickly.
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?
For a query execution tool with no annotations and no output schema, the description provides adequate but minimal information. It covers the read-only constraint and acceptable statement types, but doesn't address result format, error conditions, or performance characteristics that would help an agent use it effectively.
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%, so the schema already fully documents both parameters. The description adds no additional parameter information beyond what's in the schema descriptions. The baseline of 3 is appropriate when the schema does all the parameter documentation work.
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 verb 'execute' and resource 'SQL query', specifying it's for read-only operations with explicit statement types (SELECT, SHOW, DESCRIBE, EXPLAIN, WITH). It distinguishes from siblings like describe_table and get_schema by focusing on arbitrary query execution rather than metadata retrieval.
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?
The description provides clear context about when to use this tool (for read-only SQL queries) and implicitly distinguishes it from siblings by not being for connection management or schema inspection. However, it doesn't explicitly state when NOT to use it or name specific alternatives for different query types.
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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