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nahmanmate

PostgreSQL MCP Server

by nahmanmate
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TypeScript
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: analyze_database focuses on configuration and performance analysis, debug_database targets issue troubleshooting, and get_setup_instructions provides installation guidance. There is no overlap in functionality, making it easy for an agent to select the appropriate tool without confusion.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (analyze_database, debug_database, get_setup_instructions), using snake_case throughout. The naming is predictable and readable, with no deviations or mixed conventions.

    Tool Count2/5

    With only 3 tools, the server feels thin for a PostgreSQL domain, which typically involves operations like querying, inserting, updating, or managing tables. While the tools cover analysis, debugging, and setup, the lack of core database interaction tools suggests an incomplete surface for typical agent workflows.

    Completeness2/5

    The tool set is severely incomplete for a PostgreSQL server, as it lacks basic CRUD operations (e.g., execute_query, create_table, insert_data) and management functions (e.g., list_tables, backup_database). This will cause significant agent failures when attempting to interact with the database beyond setup and diagnostics.

  • Average 2.9/5 across 3 of 3 tools scored.

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

    • No issues in the last 6 months
    • No commit activity data available
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under AGPL 3.0.

  • 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

  • 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 'Debug common PostgreSQL issues', lacking details on behavior such as what the tool does (e.g., runs diagnostics, generates reports, modifies settings), permissions required, side effects, or output format. It doesn't disclose if it's read-only, destructive, or has rate limits, which is a significant gap for a debugging 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 waste, front-loaded and appropriately sized for its purpose. It avoids redundancy and is structured to convey the core idea without 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?

    Given the complexity of debugging (potentially involving diagnostics, analysis, or fixes), no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns, how it handles different issue types, or behavioral traits, leaving gaps that could hinder correct agent invocation.

    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 fully documents parameters like 'connectionString', 'issue' with enums, and 'logLevel'. The description adds no meaning beyond this, as it doesn't explain parameter interactions or provide examples. Baseline 3 is appropriate since the schema handles the heavy lifting.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description 'Debug common PostgreSQL issues' states a general purpose but lacks specificity about what debugging entails (e.g., diagnostics, fixes, logs) and doesn't clearly distinguish from sibling tools like 'analyze_database' or 'get_setup_instructions'. It's vague about the verb 'debug'—whether it analyzes, reports, or resolves issues.

    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 like 'analyze_database' or 'get_setup_instructions'. The description implies usage for PostgreSQL issues but doesn't specify contexts, prerequisites, or exclusions, leaving the agent to infer 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?

    With no annotations provided, the description carries the full burden of behavioral disclosure but only states what the tool does without detailing traits like whether it's read-only, requires specific permissions, has rate limits, or what the output format might be. 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, efficient sentence that directly states the tool's purpose without any unnecessary words or fluff. It is appropriately sized and front-loaded, 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 database analysis, lack of annotations, and absence of an output schema, the description is insufficient. It doesn't explain what the analysis entails, what results to expect, or any behavioral traits, leaving the agent with incomplete context for effective tool 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 schema description coverage is 100%, meaning the input schema already documents both parameters ('connectionString' and 'analysisType') with descriptions and an enum. The description adds no additional meaning beyond what the schema provides, so it meets the baseline score of 3 for adequate but unenhanced parameter information.

    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 ('analyze') and resource ('PostgreSQL database configuration and performance'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'debug_database' or 'get_setup_instructions', 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 'debug_database' or 'get_setup_instructions'. It lacks any context about prerequisites, such as needing a valid connection string, or exclusions, leaving 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. It states the tool provides 'step-by-step instructions,' implying a read-only, informational output, but doesn't clarify aspects like response format, potential side effects, or error handling, which are important for a tool with parameters.

    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 ('Get step-by-step PostgreSQL setup instructions') with zero wasted words, 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 moderate complexity (3 parameters, no annotations, no output schema), the description is minimally adequate. It covers the purpose but lacks details on behavior, usage context, or output, leaving gaps that could hinder effective tool selection and invocation.

    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 documents all parameters (version, platform, useCase) with descriptions and enums. The description adds no additional parameter details beyond implying setup instructions, which aligns with the schema but doesn't enhance it, meeting the baseline for high 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 clearly states the action ('Get step-by-step... instructions') and resource ('PostgreSQL setup'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'analyze_database' or 'debug_database', which likely serve different purposes but aren't contrasted here.

    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. The description lacks context on prerequisites, timing, or comparisons to sibling tools, leaving the agent without usage direction beyond the basic purpose.

    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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