dbecho

by ginkida

Overview Schema Related Servers Score Discussions

Python

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Server Quality Checklist

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Latest release: v0.1.1
Disambiguation5/5
Each tool has a clearly distinct purpose: profiling (analyze) vs anomaly detection (anomalies) vs schema exploration (schema) vs time series (trend) etc. No two tools appear to do the same thing, and descriptions make boundaries clear.
Naming Consistency4/5
All names use lowercase with underscores for multi-word terms, but the part-of-speech varies (verbs like analyze, query vs nouns like anomalies, schema). The pattern is mostly predictable, but lacks the strict verb_noun consistency seen in higher-scoring sets.
Tool Count5/5
11 tools is squarely in the optimal range for a database analysis server. Each tool addresses a specific analytical need without redundancy, and the number feels complete without being overwhelming.
Completeness5/5
The tool surface covers all major database exploration tasks: listing, schema, profiling, sampling, querying, comparison, anomaly detection, time trends, and health checks. No obvious gaps for the stated purpose of database analysis.
Average 3.9/5 across 11 of 11 tools scored. Lowest: 3.1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 5 commits in the last 12 weeks
- Last stable release on July 27, 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.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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?
No annotations are provided, so the description must fully inform about behavioral traits. It only states 'Show', implying a read operation, but does not disclose potential side effects, permissions, or safety guarantees.
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, consisting of two short sentences that convey the purpose and parameter. Every sentence adds value, with no redundant information.
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, the description does not need to detail return values. It covers the tool's core function and parameter. However, missing usage guidelines and behavioral transparency means completeness is only moderate for a tool with a single parameter.
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 coverage is 0% for the single parameter 'database'. The description adds meaning by stating it is 'Name of the database from config', which goes beyond the schema's minimal title. However, it lacks clarity on what config refers to and any format constraints.
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 shows an entity-relationship diagram as text, listing tables, primary keys, and foreign key relationships. The verb 'Show' and specific resource 'entity-relationship diagram' provide a clear purpose, though it does not explicitly differentiate from siblings like 'schema' or 'summary'.
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 on when to use this tool versus alternatives (e.g., 'schema' or 'summary') is provided. The description lacks any context about prerequisites or selection criteria.
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 full burden. It does not disclose whether the tool is read-only, its performance impact (e.g., full table scan), required permissions, or any side effects. The output list is given but behavioral traits are absent.
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 short (two sentences plus an Args block) and efficiently conveys the tool's purpose. However, the Args section is minimally formatted and could be more streamlined. Overall, every sentence adds value.
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 and the presence of an output schema, the description covers core functionality and arguments. However, it lacks context about when to use this tool versus siblings, potential performance implications, and any prerequisites, leaving important gaps for an AI agent.
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?
With 0% schema description coverage, the description must compensate but only adds trivial context: 'Name of the database from config' and 'Name of the table to analyze'. This does not provide format, constraints, examples, or validation beyond the schema's bare titles.
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 profiles a table with a detailed list of statistics (row count, column types, null percentages, etc.), which is a specific verb+resource combination. It distinguishes from sibling tools like 'schema' (column definition only) or 'summary' (possibly different aggregation).
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 profiling a table is needed but provides no explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives among siblings. The user must infer context from sibling tool names.
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 implies a read-only operation but does not explicitly state that it is non-destructive or requires no special permissions. For a simple list operation, the disclosure is adequate but lacks detail.
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 with no unnecessary words. It is perfectly concise and front-loaded with the verb and resource.
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 has no parameters and an output schema exists, the description is complete enough. It states what is listed (databases and descriptions). A slight improvement could mention ordering or scope, but it is adequate for a simple list 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?
There are no parameters, and schema description coverage is 100% (trivially). The description adds no parameter information, but the baseline is 3 when coverage is high.
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 'List all configured PostgreSQL databases with their descriptions.' It uses a specific verb ('List') and resource ('databases'), and distinguishes from sibling tools that focus on analysis, queries, or schema details.
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 does not provide explicit guidance on when to use this tool versus alternatives. The context of listing all databases is implied, but no usage conditions, prerequisites, or exclusions 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?
No annotations provided, so description carries full burden. It implies read-only operation but does not explicitly state non-destructive nature or other behavioral traits like rate limits or auth needs. Some context on limit is provided.
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, one paragraph with an args list. Front-loaded with main purpose, no unnecessary words.
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 output schema exists, description is fairly complete: explains purpose, parameter constraints. Missing mention of how rows are selected (random/first) but adequate for a simple 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 coverage is 0%, so description must compensate. It adds meaning for 'limit' (default 5, max 50) but only names 'database' and 'table' without further explanation. Partial compensation, not fully detailed.
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 shows sample rows from a table to understand data format, using specific verb 'show' and resource 'sample rows'. It distinguishes from siblings like 'query' or 'summary'.
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 understanding data format but does not explicitly state when to use this tool versus alternatives like query, schema, or summary. No exclusions or when-not-to-use 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?
No annotations are provided, so the description bears full weight. It describes what anomalies are detected but does not explicitly state that the tool is read-only, nor does it mention permissions, rate limits, or 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 focused sentence followed by a concise Args list. Every word adds value, and the purpose is front-loaded with no unnecessary 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?
Given the low complexity (2 params) and existence of an output schema to explain return values, the description covers the main purpose and arguments. However, it could briefly mention that the tool is non-destructive.
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?
Schema coverage is 0%, but the description adds meaningful context for both parameters: 'Name of the database from config' and 'Name of the table to check', which goes beyond the schema's title and type.
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 'Find data quality issues in a table' with a specific verb and resource, and lists concrete examples like high null rates, single-value columns, etc., which distinguishes it from sibling tools like analyze or query.
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 data quality checks but provides no explicit guidance on when to use this tool versus alternatives like analyze or sample, nor does it mention when not to use it.
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?
Without annotations, the description carries full burden. It discloses read-only nature and allowed statements, but lacks details on error handling, timeouts, authentication requirements, or response pagination.
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 succinct with three clear sentences plus parameter breakdown. Purpose is front-loaded, and every word adds value without repetition or fluff.
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 input schema and presence of an output schema, the description covers essential behavioral and usage aspects. It could mention error scenarios or result limits, but overall is sufficient for an agent to understand the tool's function.
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?
Schema description coverage is 0%, but the description includes an Args section that explains both parameters (database from config, sql is read-only query), adding necessary context beyond the schema's type information.
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 read-only SQL query and returns formatted tables. It specifies allowed statement types (SELECT, WITH, EXPLAIN, SHOW), distinguishing it from siblings like analyze or schema which provide predefined analyses.
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 indicates when to use (raw SQL queries) and the read-only restriction, but does not provide guidance on when not to use it or how it compares to siblings like list_databases or sample for specific data exploration needs.
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 must convey behavior. It states 'check connectivity and basic info', which implies a read-only operation. No additional behavioral details are given, but the simplicity of the tool means this is adequate.
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 with no extraneous information, perfectly front-loaded.
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 zero parameters and an output schema exists, the description fully covers what the tool does. No additional details are necessary for a simple health check tool in this context.
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 input schema has zero parameters, so schema description coverage is trivially 100%. No parameter info is needed, so baseline for zero parameters is 4.
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 checks connectivity and basic info for all configured databases, using a specific verb and resource. It distinguishes from sibling tools like query, analyze, etc., which are for data operations rather than connectivity checks.
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 as a health check, but no explicit guidance on when to use this versus alternatives, nor any exclusions or prerequisites.
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. It explains the tool's behavior: grouping by time period, computing counts, averages, or totals, and clarifies that omitting 'value_column' yields counts only. It does not mention auth needs, rate limits, or side effects, but the read-only nature is implied.
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 with a clear opening sentence followed by parameter explanations in a structured list-like format. It avoids redundancy and focuses on essential details, though the structure could be slightly improved with bullet points.
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 presence of an output schema (not shown), the description need not detail return values. It covers the core functionality and parameter semantics adequately. However, it could mention potential output format or restrictions (e.g., only numeric value_column) for fuller context.
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?
Schema description coverage is 0%, so the description must compensate. It explains each parameter: 'database', 'table', 'date_column', 'value_column' (optional with aggregation types), and 'period' (enumeration of groupings with default). This adds significant meaning beyond the bare 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 tool analyzes time series data by grouping rows by time period and showing counts, averages, totals. It uses specific verbs and resources ('Analyze time series data', 'group rows by time period') and distinguishes itself from sibling tools like 'analyze' or 'compare' by its focus on temporal aggregation.
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 time-based data analysis but does not explicitly state when to use it versus alternatives like 'analyze' or 'compare'. No exclusions or when-not-to-use guidance is provided, leaving the agent to infer context from the tool name and parameters.
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 must carry the burden. It correctly indicates a read operation ('Get') with no destructive hints. However, it does not mention any potential size limits, performance impacts, or authentication requirements.
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 two sentences long, with the purpose stated upfront. Every sentence adds value: the first specifies what the tool returns, the second explains the parameter usage. No unnecessary 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?
The description specifies the return content (tables, columns, types, primary keys, row counts, sizes) without needing to duplicate an output schema. It also references a sibling tool (list_databases) for prerequisite info. Given the tool's complexity, this is fully adequate.
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?
Although the input schema itself has no parameter description (coverage 0%), the tool description explicitly documents the parameter: 'database: Name of the database from config (use list_databases to see available).' This adds meaning beyond the schema's type-only definition.
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 'Get the full schema of a database' with specific components (tables, columns, types, primary keys, row counts, sizes). This verb+resource clarity distinguishes it from siblings like list_databases (which lists database names only) and query (which runs queries).
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 implicitly advises using list_databases first to see available databases, providing a clear usage context. No explicit when-not or alternative tools for similar tasks, but for this simple informational tool, the guidance is sufficient.
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 full burden. It adds critical context beyond the schema: the SQL must be SELECT. It also implies the comparison is side-by-side. Missing details on error handling or result format, but output schema likely covers returns.
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 short and front-loaded with the main purpose. The Args format is not typical JSON but still clear. Every sentence adds value. Could be slightly more formal but overall concise.
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 complexity (comparison across databases) and presence of an output schema, the description covers the essential behavior: it runs a SELECT query and compares results. It does not detail output structure, but the output schema handles that.
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 coverage is 0%, so the description must explain parameters. It does so thoroughly: 'sql' is a SELECT query, 'databases' is an optional list (default all). This adds significant meaning beyond the schema's type-only definitions.
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: 'Run the same SQL query across multiple databases and compare results side by side.' It uses a specific verb ('Run', 'compare') and resource ('SQL query', 'databases'), and distinguishes from siblings like 'query' (single database) and 'analyze' (analysis).
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 explains when to use the tool (to compare across databases) and notes that omitting 'databases' runs on all databases. However, it does not explicitly state when not to use it (e.g., for single-database queries, use 'query'), though this is implied.
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?
Without annotations, the description carries the full burden. It clearly states the tool is read-only ('get') and lists the returned data points (table counts, total rows, etc.). It does not disclose whether data is real-time or cached, but the succinct listing of outputs is sufficient for a straightforward overview 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, well-structured sentence that front-loads the main purpose ('Get a quick overview of all databases') followed by specific items. Every word adds value, and it is perfectly sized for the tool's simplicity.
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 zero parameters and the presence of an output schema (which likely details the return structure), the description is complete. It explains the tool's purpose and the key data it provides, leaving no ambiguity. The tool is simple, and the description covers all necessary context.
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 there is nothing to explain beyond what the schema (empty) shows. The description correctly implies no input is needed. Baseline 4 is appropriate as no additional parameter information is required.
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 clear verb 'get' and specifies the resource 'overview of all databases' with concrete elements: table counts, total rows, largest tables, database sizes. It distinguishes itself from sibling tools like 'list_databases' (which likely only lists names) and 'schema' (which provides structural details).
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 phrase 'quick overview' implies a lightweight, informative tool for initial understanding. Although no explicit when-to-use or when-not-to-use guidance is provided, the context is clear due to the tool's simplicity and zero parameters. A slight deduction for not mentioning alternatives, but siblings are obvious.
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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