localecheck

by qinisolabs

Overview Schema Related Servers Score Discussions

Python

Local

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool targets a distinct locale-related function (currency, holiday, address, date, tax, phone) with no overlap.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case, making the set predictable.
Tool Count5/5
Seven tools is well-scoped for a locale utility server, covering formatting, validation, and lookups without being excessive or too few.
Completeness4/5
Covers key locale operations (currency, dates, addresses, phones, holidays, taxes) but lacks timezone or unit conversion, which are minor gaps.
Average 4.2/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 11 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under Apache 2.0.
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.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses the behavior of finding the next holiday on/after a date with default today, and mentions country/subdivision options. But it does not specify output format (e.g., what is returned) or potential side effects. This is adequate but lacks some behavioral context.
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 concise sentences that front-load the purpose and use case, then provide parameter details. 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?
No output schema exists, so description should explain return value. It does not mention what the tool returns (e.g., date, name, object). Given the complexity (3 optional parameters) and sibling tools, this omission makes it less complete.
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 description must compensate. It explains 'country is GB or US' and 'subdiv narrows to a UK nation or US state', adding meaning. However, the 'after' parameter is only implied via 'on or after a date (default today)' without specifying format or constraints, leaving some ambiguity.
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 'Find the next public/bank holiday on or after a date' with verb 'find' and resource 'next public/bank holiday'. It implies differentiation from sibling 'is_holiday' by specifying use for 'next holiday' vs checking a specific date.
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?
Explicitly states 'USE THIS to answer when is the next holiday or find the next working day', providing clear context. However, it does not explicitly state when not to use it or mention alternatives like 'is_holiday'.
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 the description carries full burden. It explains that formatting respects locale for symbol position and separators, with an example. However, it does not disclose edge cases like negative amounts or large numbers, or the exact return type (string implied). Adequate but not thorough.
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 concise sentences plus an example. No wasted words. Purpose and usage are front-loaded. Every sentence contributes meaningful guidance.
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 simple 3-parameter tool with no output schema, the description is nearly complete. It covers purpose, usage guidance, behavioral details, and an example. Missing explicit return type but that is inferred from the example.
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 add meaning. It explains how parameters (amount, currency, locale) affect output via the example and mention of locale rules. It adds value beyond the schema by illustrating behavior, though each parameter is not individually described.
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 formats a money amount per locale expectations, with a specific verb ('format') and resource ('money amount'). The example and context distinguish it from sibling tools (holidays, address, date, tax, phone), which are unrelated.
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?
Explicitly says 'USE THIS whenever you show a price/total to a user or put one in an email, invoice, or report.' It defines when to use but does not mention alternatives or when not to use, though the context makes it clear.
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?
With no annotations, the description carries the full burden. It discloses that GB defaults to England and passes subdiv codes for UK nations or US states. However, it does not specify the return type (likely boolean) or behavior for invalid dates, unsupported countries, or error cases. This is adequate but lacks depth.
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, front-loaded with the core function and format, followed by usage guidelines and parameter details. Every sentence adds value with no wasted 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 no output schema and 3 parameters, the description covers usage context and parameter semantics well. It mentions subdivisions for GB and US but does not explicitly state that only GB and US are supported, nor does it describe the return value. This is still complete enough for a simple check tool.
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 has 0% description coverage, so the description compensates well. It explains the date format, country default (GB), and provides examples for subdiv (SCT, WLS, NIR, US state code). It adds meaning beyond the schema but could be more precise by noting that only GB and US are supported.
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 checks if a date is a public/bank holiday, specifies the date format YYYY-MM-DD, and mentions use cases like business-day deadlines and delivery SLAs. It distinguishes from siblings like next_holiday by focusing on checking a specific date.
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 explicitly says 'USE THIS when computing business-day deadlines, delivery SLAs, or next working day', providing clear context. It also gives examples of subdivisions (SCT, WLS, NIR, US state code) but does not explicitly mention when not to use it or directly name alternatives like next_holiday.
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. Mentions returns a confidence flag, but does not disclose behavior for non-UK/US addresses, error handling, or performance. Adequate but leaves room for improvement.
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: first states purpose, second gives use cases and output. No wasted words, front-loaded with key 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?
For a single-parameter tool with no output schema and no annotations, the description covers purpose, use cases, and output (confidence flag). Missing details on confidence flag semantics and error behavior, but overall sufficient for an AI agent to use 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?
With 0% schema description coverage, the description adds critical meaning: 'free-text UK or US address'. This compensates well for the bare schema, though could specify input length or encoding.
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?
Clearly states the verb 'Extract' and resource 'structured {country, postcode, city} from a free-text UK or US address'. Distinguishes from siblings like parse_date, format_currency, etc., which are unrelated.
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?
Explicitly tells when to use: 'when onboarding a user, running a KYC/fraud check, or storing an address'. Advises against manual splitting. Although no explicit alternatives, siblings are unrelated, so guidance is clear.
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 burden. It discloses that the tool returns 'valid:false for impossible dates,' but does not describe the full return format or behavior on success (e.g., whether it returns the parsed ISO string). This is a minor gap.
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 no waste. The first sentence states the core purpose, the second gives usage guidance, and the third provides a behavioral detail. It is front-loaded and efficient.
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 two parameters, no output schema, and no annotations, the description is largely complete. It covers purpose, usage, and a return indicator. However, it does not explicitly state the output format on success (implied by the first sentence) or any prerequisites.
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%, so the description must add meaning. It explains the 'input' parameter as a human-written date and 'locale' as 'en-GB' or 'en-US', with an example of ambiguity. It adds value beyond the schema, though it could explicitly state the default locale and optionality.
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 action: 'Convert a human-written date into ISO 8601 (YYYY-MM-DD).' It specifies the input and output format, and the purpose is distinct from sibling tools like format_currency or parse_address.
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 explicitly advises when to use ('USE THIS whenever you need to interpret a date a person typed') and highlights ambiguity with numeric dates, including locale sensitivity. It does not explicitly exclude usage scenarios, but the guidance is clear for the primary use case.
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?
Discloses that the tool is date-sensitive, handles historical/temporary changes, and explains US behavior (returns 0 without state code). With no annotations, the description effectively conveys key behavioral traits beyond the schema.
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 (4 sentences) with no redundant information. It front-loads the primary purpose, then adds key details about date-sensitivity and US handling.
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?
Covers usage for both GB and US, parameter guidance, and date-sensitive nature. However, it does not describe the output format (e.g., numeric rate, currency symbol), which could be inferred but is not explicit.
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?
Despite 0% schema coverage, the description explains each parameter: country defaults to GB, date is the invoice date, state is for US. It provides context for when to use each, compensating for the lack of schema descriptions.
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 'Look up the correct consumption-tax rate' and specifies it's for VAT/sales tax. It distinguishes from sibling tools (none cover tax rates) by providing precise use-case and geographic variations.
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?
Explicitly says 'USE THIS before calculating VAT or sales tax on an invoice or quote — never recall the rate from memory', giving clear when-to-use advice. It does not mention alternatives, but no sibling tool provides tax rates, so 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.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description bears full burden. It discloses normalization to E.164 and returns valid:false for invalid numbers, but does not explicitly describe the return structure for valid numbers (e.g., whether it returns the normalized number or a full object). Lacks some completeness.
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 with immediate clarity: first sentence defines purpose, second gives usage and return info. 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?
For a simple validation tool with no output schema, the description covers essential aspects: purpose, usage, parameters, and basic behavior. Slightly incomplete on return value details but generally 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?
With 0% schema description coverage, the description adds meaning by specifying that 'number' is the raw input and 'region' should be 'GB' or 'US' with default 'GB'. Adds useful context beyond the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool validates and normalizes phone numbers to E.164 format, with an example. It effectively distinguishes from siblings by being the only phone validation tool.
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 advises using this tool before saving, dialling, or sending SMS, and instructs to pass region 'GB' or 'US'. Provides strong usage guidance without ambiguity.
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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