pgverdict

by Svaca33

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct concern: profile listing, query-time ranking, query plan analysis, and index inefficiency. There is no overlap in purpose; list_hotspots and explain_query are complementary rather than duplicative.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in lowercase snake_case (list_profiles, list_hotspots, explain_query, find_dead_weight). This makes the API predictable and easy to navigate.
Tool Count5/5
With 4 tools, the server is well-scoped for a specialized PostgreSQL performance diagnostic. It covers the essential workflow without unnecessary bloat or thinness.
Completeness4/5
The set covers the primary diagnostic lifecycle: identify hotspots, explain query plans, and find wasteful indexes. Minor gaps such as a general database overview are acceptable given the focused purpose, and no critical dead-end operations are missing.
Average 4.4/5 across 4 of 4 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
- 8 commits in the last 12 weeks
- Last stable release on August 9, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
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  ]
}
```
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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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden and discloses key behaviors: query texts are normalized with placeholder literals, and the ranking is based on frequency x cost. It explicitly notes that placeholders are not real instructions, for safety. However, it does not explicitly state whether the tool is read-only or has 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 concise and well-structured with two compact paragraphs. The main purpose is front-loaded, and the additional context about normalized queries is essential without being verbose.
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?
An output schema is present, so return values are covered. The description captures the core purpose and important caveats, but it lacks parameter-level details and does not elaborate on how limit or min_calls affect the results. For a relatively simple tool, this is adequate but not complete.
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%, and the description does not explain the meaning of 'profile', 'limit', or 'min_calls'. While the parameter names and defaults hint at their roles, 'profile' is ambiguous and no parameter-specific guidance is provided, so the agent must infer from names 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?
Description clearly states the tool ranks queries by total execution time (frequency x cost), which is a specific verb+resource. It also distinguishes itself as step 1 (IDENTIFY) of the verification loop, setting it apart from sibling tools like explain_query and find_dead_weight.
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 frames when to use this tool by labeling it as the IDENTIFY step, indicating it shows where time goes rather than how to fix it. It also warns that query texts are normalized and placeholders must not be treated as instructions, providing essential usage guidance.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions returning 'observed executions (slowest first)', redacting text values unless 'redact = false', and requiring a copied/mounted log file. It does not discuss permissions or error handling, but covers key operational behaviors.
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 three sentences with the main purpose front-loaded. Each sentence adds distinct value: purpose, prerequisites, output behavior, and integration. There is no verbosity or redundancy.
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 description covers prerequisites, output ordering, redaction, and downstream integration. However, with 0% schema coverage, missing explanations for 'sql' and 'queryid' create a clear gap in how to identify the query. The output schema exists, so return structure is handled, but parameter semantics remain incomplete.
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%, so the description must compensate. It only clarifies 'log_path' ('pass the path to the (copied or mounted) log file') and hints at profile-based redaction. The roles of 'sql' and 'queryid' are never explained, leaving significant ambiguity in how to target a specific query.
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 'Recover REAL parameter values for a query from an auto_explain log', using a specific verb and resource. It distinguishes itself by claiming it is 'always a better grounding source than statistics synthesis' and by noting integration points with sibling tools like explain_query and verify_index.
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?
It provides explicit conditions for use: 'Requires auto_explain with log_format='json' and log_parameter_max_length != 0' and recommends feeding results into explain_query or verify_index. It positions itself as superior to statistics synthesis but does not explicitly state when not to use it, so it falls just short of a 5.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly states the tool is non-destructive ('does not drop anything and never will'), which is key safety information. It also warns about the stats_since window causing false positives, and mentions the pgstattuple dependency. This is rich behavioral context, though it could go further (e.g., auth requirements, but that's likely shared across siblings).
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 concise and front-loaded: the first sentence immediately states the tool's purpose and what it finds. The second paragraph adds essential caveats (non-destructive, stats_since) without unnecessary fluff. Every sentence earns its place.
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 fairly complete for a read-only analysis tool. It covers the tool's scope, safety profile, and a key caveat (stats_since). The output schema exists, so return values are already documented. It doesn't explain the profile parameter, but that's a minor gap given the overall 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?
The schema has one parameter 'profile' with zero description coverage. The tool description does not explain what 'profile' means, its format, or how it relates to the tool's function. Given the low schema coverage, the description should compensate but doesn't, leaving the agent to guess or infer from sibling tools like list_profiles.
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: 'Find indexes that cost writes but earn nothing' and enumerates specific categories (never-scanned, exact duplicates, prefix-redundant pairs, low leaf density). This specific verb+resource combination distinguishes it from sibling tools like list_hotspots or explain_query.
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 use: these are observations, safe quick wins for review. It also gives a caution about stats_since and clarifies that pgverdict never drops anything. However, it doesn't explicitly name alternatives or state when not to use this tool versus siblings, so it lacks full exclusion/alternative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral disclosure. It explicitly states that the clone is destroyed afterwards always, the operation takes tens of seconds to minutes, and it fails loudly when measurement is impossible. This is substantial, honest transparency beyond basic mutation/read hints.
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 front-loaded with the core purpose and verdict, then follows with evidence details and operational requirements. Every sentence delivers relevant information—purpose, evidence types, prerequisites, execution time, and failure behavior—with no wasteful filler.
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?
For a complex tool with no annotations, the description covers prerequisites, execution time, side effects (clone destruction), failure modes, and verdict logic. An output schema exists, so return values need not be described. However, the optional parameters sqls, queryids, and regression_top_n are not explained in relation to the tool's logic, leaving some invocation ambiguity.
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%, so the description must compensate, but it does not clearly explain parameter meanings. Terms like 'candidate index' and 'top production queries' only loosely map to index_def and queryids/sqls; regression_top_n and profile are not explicitly tied to their parameters. The schema names provide some clues, but the description adds little direct parameter guidance.
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 opens with 'MEASURE a candidate index on a disposable data-bearing clone and return a verdict,' which identifies a specific action, resource, and outcome. It distinguishes itself from siblings by stating 'This is the only tool that produces evidence' and enumerating the exact evidence produced.
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 clearly states when this tool should be used (when evidence is needed) and calls out prerequisites (clone_provider='docker', Docker running). It implicitly differentiates from sibling tools by claiming exclusivity for evidence production, though it does not explicitly cite alternative tools or when-not-to-use conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden, and it excels. It discloses that results are simulated, with evidence_level 'simulated', and explicitly names limitations (write amplification, regressions on other queries, real timings unknown). It further warns not to present candidates as advice. This is thorough behavioral disclosure beyond any default expectations.
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 well-structured: a concise opening sentence establishes the core purpose, followed by a clearly separated warning block that earns its place. Every sentence provides critical context (simulated evidence, limitations, alternative tool, usage caution). No fluff or redundancy.
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 complex tool with output schema and no annotations, the description is remarkably complete. It covers the action, the methodology (HypoPG, pg_stats, typical/worst/selective cases), the evidence level, and the critical caveats. The output schema handles return-value details, so no further description is needed there. It also situates the tool among siblings by naming verify_index.
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%, and the description does not compensate by explaining what each parameter (sql, profile, queryid) means or how they relate to the tool's operation. While the names are somewhat intuitive, the description must add meaning beyond the schema, especially with 0% coverage. It mentions 'for a query' and 'parameter values' but never links these to the actual input parameters.
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 function: generating candidate indexes and cost-simulating them with HypoPG, using pg_stats-grounded parameter values. It explicitly distinguishes itself from verify_index, which is the only tool that produces a real accept/reject verdict. This is a specific, actionable purpose with clear differentiation from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit usage context: this tool is for simulated exploration only, not for final recommendations. It states clear when-not-to-use conditions (write amplification, regressions, real timings unknown) and points to verify_index as the alternative for real-data measurement. This is excellent guidance on when to use versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully carries the burden of behavioral disclosure. It explicitly states that without analyze the query is never executed, that analyze actually runs the query but only as SELECT-only and only on profiles that opt in, and that the output is not a recommendation. This is exemplary 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 four sentences long, front-loaded with the main purpose, and each sentence adds distinct value. It avoids redundancy, making every line earn its place.
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 highly complete given the output schema exists, covering purpose, behavior, safety, and parameter semantics. The only minor gap is that it does not explicitly state that sql and queryid are mutually exclusive or that at least one must be provided, which could cause confusion for an agent constructing a call.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It does so thoroughly: sql and queryid are explained as the two lookup methods, params are described as representative values for parameterized queries, analyze is explained as the execution toggle, and profile is contextualized via per-profile allow_analyze gating. This fully compensates for the empty 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 opens with a precise verb and resource: 'EXPLAIN a query (by literal SQL or by pg_stat_statements queryid) and read the plan in plain language.' It clearly states the tool's function and its two input modes, distinguishing it from the sibling tools (list_profiles, list_hotspots, find_dead_weight) which serve different purposes.
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 when to use estimate mode versus analyze mode, explains that analyze is gated per profile, and advises passing params for parameterized queries. It does not explicitly name alternative tools, but the guidance for using this tool effectively is strong, leaving no ambiguity about its intended use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full behavioral burden and fully delivers. It discloses that CREATE INDEX runs CONCURRENTLY, that non-ACCEPT verdicts are refused without an explicit override, that overrides are permanently stamped into the comment, and that the tool never executes migrations. These are non-obvious behaviors that significantly affect usage.
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?
Four sentences deliver purpose, formats, key behaviors, and usage constraints without redundancy. The structure is front-loaded with the primary action, then details, then an operational note. Every sentence earns its place; there is no filler.
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?
The description covers the essential aspects for a migration-generation tool: what it produces, allowed formats, the critical behavior around verification status, the override mechanism, and the safety guarantee of not executing migrations. Given an output schema exists, the description needn't detail return structure. It provides enough context for an agent to invoke the tool correctly and handle the result appropriately.
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 0%, so the description must compensate. It explicitly provides valid format values (sql, alembic, efcore) and explains the behavioral impact of allow_unverified (override stamped permanently). However, it does not explain the 'profile' parameter or explicitly tie 'verification_id' to the source record beyond the purpose statement. The description partially compensates but leaves some parameters underspecified.
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 opens with a specific verb and resource: 'Generate a migration FILE from a verify_index verification record.' It clearly distinguishes the tool from its siblings (like verify_index or propose_index) by focusing on migration generation. The mention of output formats (sql, alembic, efcore) further clarifies its purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when the tool is appropriate: it works from a verify_index verification record. It also gives a concrete exclusion criterion: 'Refuses non-ACCEPT verdicts unless allow_unverified=true.' The guidance to save the content rather than execute it ('pgverdict never executes migrations') provides clear operational instructions, effectively differentiating this tool from potential alternatives.
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?
With no annotations, the description carries the burden. It transparently states this is a listing operation and adds a key behavioral constraint (all other tools depend on this profile list). It doesn't discuss side effects, but none are expected for a list operation. The dependency warning is valuable context beyond a simple 'List'.
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?
Two sentences, front-loaded with the main purpose, and every sentence adds value. The second sentence explains why the tool is important and provides clear instruction. No wasted words.
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?
Given the tool's simplicity (no params, output schema present, straightforward listing), the description fully covers what the tool does, when to use it, and its role in the broader toolset. Nothing essential is missing.
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 schema coverage is trivially 100%. Per the baseline, 0 params receive a 4. The description adds no parameter details because there are none to document.
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') and a clear resource ('configured pgverdict profiles'). It clearly differentiates from siblings by being the profile-discovery tool, while siblings list hotspots, explain queries, and find dead weight.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use: 'Call this first', and why: 'every other pgverdict tool requires an explicit profile parameter'. Also gives a strong exclusion: 'Never guess profile names', which is actionable 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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