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RachelHuangZW

postgres-mcp

by RachelHuangZW
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool serves a distinct purpose: executing queries, explaining plans, and retrieving table schema. No overlap exists.

    Naming Consistency5/5

    All tools follow a consistent verb_noun snake_case pattern (execute_query, explain_query, get_table_schema) with clear, descriptive names.

    Tool Count4/5

    Three tools is slightly low for a database server, but the scope is focused on query execution and schema inspection, which is reasonable. Could benefit from additional tools like listing tables.

    Completeness3/5

    Core query execution and schema retrieval are covered, but missing tools to list tables or databases, manage transactions, or modify data directly. Agents would need to rely on raw SQL for many operations.

  • Average 3.4/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 16 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

  • This repository is licensed under MIT License.

  • 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 the full burden of behavioral disclosure. It only states what is retrieved (columns and indexes) but does not mention safety (read-only), required permissions, or limitations (e.g., no constraints or foreign keys).

    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—'Get columns and indexes for a PostgreSQL table.'—with no filler or redundancy. It is front-loaded and earns its place.

    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, the output schema exists (so return values need not be described), but the description omits important context like default behavior of the 'schema' parameter and the requirement that the table must exist. It is minimally complete.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters1/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, and the description adds no information beyond the parameter names. It does not explain that 'schema' defaults to 'public' or that 'table_name' is case-sensitive, providing no semantic value over the input schema.

    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 'Get' and the resource 'columns and indexes for a PostgreSQL table'. It distinguishes from siblings 'execute_query' (execute SQL) and 'explain_query' (show query plan) by specifying exactly what artifact is retrieved.

    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 'execute_query' or 'explain_query'. There is no mention of prerequisites (e.g., table must exist) or context in which this tool is preferred.

    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 exist, so the description must disclose behavioral traits. It mentions JSON output but fails to indicate whether the query can modify data (read-only vs write), what side effects occur, or any authorization requirements. This is insufficient for a potentially destructive operation.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is concise (one sentence) and front-loaded with the core action. However, it lacks structure (e.g., no sections or warnings) and could be slightly more informative without losing brevity.

    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 presence of an output schema and the potential risks of SQL execution, the description is incomplete. It does not clarify read-only status, transaction behavior, error handling, or limitations. A more thorough description is needed for safe and correct agent use.

    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 0%, yet the description adds no meaning beyond the parameter name and type. There is no guidance on SQL format, statement constraints, or parameterization, leaving the agent to infer from the name alone.

    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 tool executes a SQL query against a PostgreSQL database and returns JSON results. It uses specific verbs and resources, and the sibling tools (explain_query, get_table_schema) suggest distinct purposes.

    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?

    No explicit when-to-use or when-not-to-use guidance is provided. The description implies usage for executing SQL queries, but does not contrast with siblings like explain_query or get_table_schema.

    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?

    No annotations are provided, so the description carries the full burden. It discloses the critical behavioral trait that setting analyze=True actually executes the query, but does not discuss other traits like idempotency or potential 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 extremely concise with two sentences containing no fluff. Every word adds value.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the simple tool with only two parameters and existence of an output schema, the description is largely complete. It could optionally mention what the plan looks like or any limitations, but the output schema likely covers that.

    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?

    With 0% schema description coverage, the description compensates well by explaining the 'sql' parameter implicitly and the 'analyze' parameter explicitly ('Set analyze=True to run EXPLAIN ANALYZE'). For two parameters, this adds meaningful context beyond the schema.

    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 it returns the EXPLAIN execution plan for a SQL query, which distinguishes it from sibling tools like execute_query (executes the query) and get_table_schema (retrieves schema).

    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 by mentioning the analyze flag, but it does not explicitly state when to use this tool versus execute_query or provide exclusions. It could add guidance like 'Use when you need the query plan instead of actual results.'

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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