Postgres MCP Pro

Server Quality Checklist

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Latest release: v0.3.0
Disambiguation4/5
Tools are mostly distinct with clear purposes. The main potential confusion is between analyze_workload_indexes and analyze_query_indexes, which both recommend indexes but differ by input source (workload vs. explicit query list). Other tools like list_schemas, list_objects, and get_object_details build a clear hierarchy.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in lowercase snake_case (e.g., explain_query, list_schemas, analyze_db_health). The verbs (explain, list, get, analyze) are appropriate for their actions and the naming is uniform throughout the set.
Tool Count5/5
With 9 tools, the server is well-scoped for PostgreSQL database analysis and optimization. Each tool covers a distinct aspect (schemas, objects, query plans, indexes, health, top queries, execution) without unnecessary bloat, fitting the typical ideal range.
Completeness4/5
The tool set provides strong coverage for common PostgreSQL diagnostic workflows: exploring schemas/objects, explaining queries, analyzing indexes, identifying top queries, and running health checks. Minor gaps include lack of direct table bloat analysis and no management tools (e.g., vacuum or reindex), but these are not core to the server's apparent read/analysis focus.
Average 3.6/5 across 9 of 9 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
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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?
The description adds no behavioral context beyond the readOnlyHint annotation; it simply restates the tool's purpose in a generic way. It does not mention pagination, default object_type behavior, or scope limitations, and because annotations already declare read-only, the description provides no additional transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (a single sentence) and free of fluff, but it is also under-specified and essentially repeats the title. It lacks any structured detail (e.g., parameter mentions, usage context) that would make the sentence earn its place beyond the obvious.
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 tool's simplicity, the input schema, readOnlyHint annotation, and presence of an output schema provide sufficient context for an agent to use it correctly. The description, while minimal, correctly states the high-level function, which is adequate for a straightforward list operation.
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 fully documents both parameters (schema_name required, object_type with allowed values), giving 100% schema description coverage. The description adds no parameter-level meaning, but the schema already handles the semantics, so the baseline of 3 applies.
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 identifies the action (list) and resource (objects in a schema), which is a specific verb+resource combination. However, it does not mention the object types or differentiate from sibling tools like get_object_details, so it lacks the scope and differentiation needed for a 5.
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 list_schemas or get_object_details. There is no context about prerequisites, exclusions, or typical use cases, so the agent receives no decision-support information.
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?
The annotations already declare readOnlyHint=true, so the description's read-only analysis nature is consistent. It adds context that the analysis targets 'frequently executed queries' and produces index recommendations, but does not disclose any potential performance impact, the fact that it does not apply indexes, or what happens with the output beyond being a recommendation.
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 sentence that immediately states the tool's purpose without superfluous words. It is front-loaded with the action and object.
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 presence of an output schema and full schema parameter coverage, the description is minimally sufficient. However, it lacks information on the two methods ('dta' vs 'llm') and does not clarify how this tool differs from the closely named sibling 'analyze_query_indexes', which is a notable gap for selection.
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 both 'method' and 'max_index_size_mb' having descriptions. The tool description adds no parameter-specific semantics beyond the schema, so it does not exceed the baseline.
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 uses a specific verb ('Analyze') and identifies the resource ('frequently executed queries') and outcome ('recommend optimal indexes'), making the tool's function clear. However, it does not explicitly differentiate from the sibling tool 'analyze_query_indexes', which may operate on a single query rather than a workload.
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 'analyze_query_indexes' or 'get_top_queries'. It does not mention scenarios, prerequisites, or exclusions, leaving the agent to infer usage from the name and context.
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?
The annotations include destructiveHint=true, which already alerts the agent to potential destructive side effects. The description's 'any' reinforces that the tool can execute non-read queries, but it adds no additional behavioral context such as permission requirements, transaction handling, or result format. This is a minimal contribution beyond the structured hint.
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 consists of one concise sentence, 'Execute any SQL query', which is immediately front-loaded and free of unnecessary elaboration. It optimizes brevity while conveying the essential action.
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?
The tool is a simple SQL executor with a single parameter and an output schema, and the annotation provides a destructive warning. However, the description gives no context about when to use it (e.g., for arbitrary reads/writes) versus the specialized sibling tools, and it omits any caveats about side effects beyond the bare annotation. Given the tool's low complexity, this is adequate but not enriched, so it falls short of being fully contextual.
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 provides a single `sql` parameter with a description ('SQL to run') and a default, achieving 100% coverage. The tool description 'Execute any SQL query' adds no further meaning about the parameter's syntax, allowed statements, or formatting. Since the schema already documents the parameter, no compensating explanation is needed.
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 any SQL query' clearly states the tool's verb (execute) and resource (SQL query), making its core function unambiguous. However, it does not explicitly differentiate from sibling tools like explain_query or analyze_query_indexes, leaving the reader to infer its role as a general-purpose executor. This is clear but lacks explicit 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. It doesn't mention that this is for arbitrary SQL execution while siblings handle query analysis, schema listing, or index insights, nor does it state any restrictions or prerequisites. This leaves the agent without direction for tool selection.
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?
Annotations include readOnlyHint: true, which already indicates a safe read operation, and the description aligns with this by saying 'Show detailed information'. However, the description adds no extra behavioral context beyond the annotation, such as what constitutes 'detailed' or how missing objects are handled. With annotations present, the bar is lower, and the description meets the minimum without adding value.
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, concise sentence that front-loads the action and resource. It contains no filler or redundant wording, earning full marks for conciseness.
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 tool's simplicity, an output schema (mentioned in context signals), and the fully described input schema, the description is largely adequate. However, it lacks explicit guidance on when to use this tool over siblings, which would improve completeness. The missing usage context is the main gap.
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 each parameter (schema_name, object_name, object_type) having a clear description. The tool description itself adds no parameter-specific meaning beyond the schema, so the baseline of 3 applies. object_type's default value and allowed values are already in the schema.
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 'Show detailed information about a database object' uses a specific verb ('Show') and resource ('database object'), making the core purpose clear. It distinguishes itself from sibling tools like 'list_objects' (which likely enumerates objects) by focusing on details of a single object, but it does not explicitly state this 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?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention that this tool is for inspecting a specific object as opposed to listing all objects, nor does it describe any prerequisites or exclusions. The user must infer usage from the tool name and sibling context.
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?
Annotations declare readOnlyHint: true, so the safe nature is already known. The description adds useful context about the data source (pg_stat_statements) but does not disclose prerequisites like extension availability or any limitations on the reported data.
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?
A single, clear sentence that front-loads the purpose and names the data source. No wasted words or redundant information.
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?
The description is simple and self-contained, naming the data source and purpose. Output schema and parameter schema cover the remaining details. A minor gap is not mentioning that the pg_stat_statements extension must be installed for the tool to work.
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 provides 100% coverage for both parameters, including explanations for valid sort_by values. The tool description adds no extra parameter-level detail, so the baseline score of 3 is appropriate.
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 action ('Reports') and the resource ('slowest or most resource-intensive queries') with a specific data source ('pg_stat_statements'). This distinguishes it from siblings like explain_query and analyze_query_indexes, which focus on different aspects.
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?
There is no explicit guidance on when to use this tool versus the sibling analysis tools. No mention of prerequisites, appropriate contexts, or exclusions; the intended use is only implied by the name and description.
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?
The readOnlyHint annotation already signals this is a safe read operation. The description adds the 'up to 10' query limit, which is a useful constraint, but does not disclose other behaviors such as the default analysis method ('dta'), the effect of 'max_index_size_mb', or potential latency. It adds minimal context beyond the annotations and purpose.
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, front-loaded sentence that conveys the core purpose and a key constraint without unnecessary words. Every word earns its place, and no filler or redundancy exists.
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 output schema exists and the annotations mark it read-only, the description is adequate for an AI to understand the tool's basic function. It captures the input limit and the goal. Missing is a note on when to use this vs. analyze_workload_indexes, but that is a usage nuance rather than a completeness gap. Overall it is complete for a simple analysis tool.
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 input schema already documents all parameters. The description does not add additional meaning for 'queries', 'method', or 'max_index_size_mb' beyond what the schema provides. Baseline of 3 applies because the schema handles parameter documentation.
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 uses a specific verb ('Analyze'), names the resource ('SQL queries'), and clearly states the output ('recommend optimal indexes'). It distinguishes itself from the sibling tool 'analyze_workload_indexes' by noting the input is a list of up to 10 queries rather than a workload.
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 for a bounded set of queries (up to 10) but does not explicitly state when to prefer this tool over alternatives like 'analyze_workload_indexes' or 'explain_query'. There are no clear exclusions or alternative references, so guidance is implied rather than explicit.
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?
Annotations declare readOnlyHint=true, so the tool is known to be a safe read operation. However, the description does not disclose that the 'analyze' parameter actually executes the query, which is an important behavioral nuance. No contradiction with annotations, but little added context beyond purpose.
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?
A single, clear sentence that immediately states what the tool does. No unnecessary words or repetition.
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 rich schema (100% param coverage) and existing output schema, the description is mostly sufficient. However, it could mention the optional analyze and hypothetical_indexes capabilities in the main description for quicker agent awareness, though these are covered in the schema.
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 detailed parameter descriptions including examples for hypothetical_indexes. The description itself adds no parameter-level information, so baseline 3 applies.
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?
Description clearly states the tool explains the execution plan for a SQL query, with specific verb 'explains' and resource 'execution plan'. It also mentions cost estimates, distinguishing it from execute_sql and analyze_query_indexes.
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?
Usage context is implied ('explains the execution plan') but no explicit guidance on when to use this tool versus alternatives like analyze_query_indexes or execute_sql. No exclusions or prerequisites are mentioned.
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?
Annotations already declare readOnlyHint=true, which covers safety. The description adds the scope 'all schemas' but does not disclose other behavioral traits like ordering, pagination, or return format. With annotations present, a neutral score is appropriate.
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 concise sentence, 'List all schemas in the database', with no wasted words. It front-loads the action and object clearly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only tool with no parameters and an output schema, the description fully covers the necessary context. It is complete without over-explaining, and annotations cover the safety profile.
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, so the baseline is 4. The description adds no param info, but none is needed; the empty schema is self-explanatory.
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 uses a specific verb 'List' with a clear resource 'schemas' and scope 'in the database'. It distinguishes from siblings like list_objects and get_object_details by explicitly focusing on schemas.
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 clearly implies when to use the tool (when needing all schemas), but it does not explicitly state alternatives or exclusions. The presence of siblings like list_objects suggests a need for more differentiation, but none is provided.
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?
Annotations already declare readOnlyHint=true, and the description aligns with this by using 'analyzes' and describing read-only checks. The description adds value by explaining what each check examines (e.g., invalid indexes, connection utilization, vacuum health), which helps the agent understand the tool's behavior beyond the annotation.
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 well-structured with a lead sentence and a bulleted list of checks. It is appropriately sized given the number of checks, and each bullet adds necessary information. It is slightly long but not wasteful, earning a strong score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema present, the description does not need to explain return values. It thoroughly covers the input parameter, enumerates all health check options, and describes the default behavior, making it complete for an agent to select and invoke the tool correctly.
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 schema already lists valid values for health_type with 100% coverage. The description enhances this by explaining what each value does and explicitly noting that comma-separated lists are allowed, which is not in the schema. This additional semantic detail goes beyond the schema baseline.
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's purpose with a specific verb ('analyzes database health') and enumerates eight distinct health check categories. This distinguishes it from sibling tools like analyze_workload_indexes and execute_sql, which focus on other aspects of database operations.
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 for using the tool by detailing each health check option and the default behavior. However, it does not explicitly state when to use this tool versus alternatives like analyze_workload_indexes, nor does it mention exclusions, so it falls short of full explicit guidance.
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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