SimpleLocalize MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: create keys, duplicate translations, get environment status, get missing translations, get translations for keys, publish translations, and update translations. The descriptions clearly differentiate their functions, making misselection unlikely.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with clear, descriptive names: create_translation_keys, duplicate_translation, get_environment_status, get_missing_translations, get_translations_for_keys, publish_translations, update_translations. The naming is uniform and predictable throughout.
Tool Count5/5
With 7 tools, this server is well-scoped for a localization/translation management system. Each tool serves a specific, necessary function in the domain, covering key operations without bloat or redundancy.
Completeness4/5
The toolset covers core translation management workflows well: creation, duplication, retrieval (with various filters), updating, and publishing. A minor gap is the lack of explicit delete operations for keys or translations, which agents might need to work around, but overall coverage is strong.
Average 3.9/5 across 7 of 7 tools scored. Lowest: 3.3/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions what information is returned (keys, languages, translations, creation date, resources), which adds some context beyond basic purpose. However, it doesn't cover critical aspects like whether this is a read-only operation, potential rate limits, authentication needs, or error conditions—significant gaps for a tool with zero annotation coverage.
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 appropriately sized and front-loaded, with the core purpose stated first. The additional details and parameter explanation are relevant, though the structure could be slightly more streamlined (e.g., integrating the Args section more seamlessly). Overall, it's efficient with minimal waste.
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 moderate complexity (1 parameter, no output schema, no annotations), the description is partially complete. It covers the purpose and parameter semantics well but lacks usage guidelines and full behavioral transparency. For a status-checking tool, this is adequate but leaves gaps in guiding the agent effectively.
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?
The description adds substantial meaning beyond the input schema, which has 0% description coverage. It explains that 'environment_key' is used to check status for, provides examples (e.g., '_latest', '_production', or custom key), and clarifies its role. This fully compensates for the schema's lack of documentation, making the parameter's purpose and usage clear.
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 verb ('Get') and resource ('current status of a specified environment'), making the purpose evident. It distinguishes this as a status-checking tool among siblings that focus on translation operations (create, duplicate, get missing, etc.), though it doesn't explicitly contrast with them. The specificity is good 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?
No guidance is provided on when to use this tool versus alternatives. While the description implies it's for checking environment status, it doesn't mention prerequisites, when it's appropriate (e.g., before publishing), or what other tools might be related. This leaves the agent without contextual usage cues.
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 the full burden. It discloses some behavioral traits: bulk capability (up to 100 translations), required/optional fields, and a text length limit. However, it lacks critical details like whether this is a destructive overwrite, what permissions are needed, error handling for invalid inputs, or response format. For a mutation tool with zero annotation coverage, this is insufficient.
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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by elaboration. The 'Args:' section is well-structured but could be more integrated. Minor redundancy exists (e.g., 'single request' repeated), but overall it's efficient with minimal waste.
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 (bulk mutation), no annotations, no output schema, and low schema coverage, the description is partially complete. It covers parameter semantics well but lacks behavioral context (e.g., side effects, auth needs) and output details. For a mutation tool, this leaves gaps that could hinder agent usage.
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?
The input schema has 0% description coverage (only 'translations' as an array of objects), but the description compensates fully. It details the 'translations' parameter's structure: a list of dictionaries with required fields (key, language, text) and an optional field (namespace), including constraints like max 65535 chars for text. 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Update translations in bulk with a single request.' It specifies the verb ('update'), resource ('translations'), and scope ('in bulk'), distinguishing it from sibling tools like 'create_translation_keys' or 'duplicate_translation'. However, it doesn't explicitly differentiate from all siblings (e.g., 'publish_translations' might also involve updates).
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 mentions bulk updates but doesn't specify scenarios where bulk is preferred over individual updates (e.g., via 'duplicate_translation') or when other tools like 'publish_translations' might be more appropriate. No exclusions or prerequisites are stated.
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 provided, the description carries the full burden of behavioral disclosure. It adds useful context: it requires authorization, is only for paid plans, and describes what gets published (translations from editor or between environments). However, it lacks details on side effects (e.g., overwrites, deployment time), rate limits, or error handling, leaving gaps for a mutation 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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by elaboration on functionality, prerequisites, and parameter details. Every sentence adds value without redundancy, and the structure is clear with bullet points for environment keys.
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 (a mutation with authorization requirements), no annotations, no output schema, and 1 parameter with 0% schema coverage, the description is moderately complete. It covers purpose, usage context, and parameter semantics well, but lacks details on behavioral outcomes (e.g., what happens after publishing, error responses) and doesn't fully address all contextual needs for safe invocation.
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 description adds significant meaning beyond the input schema, which has 0% description coverage. It explains the 'environment_key' parameter with examples ('_latest', '_production', custom keys) and clarifies their purposes (e.g., '_latest' publishes from Translation Editor). This compensates well for the schema's lack of documentation, though it doesn't cover all possible edge cases or formats.
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's purpose: 'Publish translations to a specified environment.' It specifies the verb ('publish') and resource ('translations'), and distinguishes it from siblings like 'create_translation_keys' or 'update_translations' by focusing on deployment rather than creation or editing. However, it doesn't explicitly differentiate from 'get_environment_status' in terms of action vs. 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 when to use this tool: for publishing translations from the editor to environments or between environments. It mentions authorization and paid plan requirements, which are important usage prerequisites. However, it doesn't explicitly state when not to use it or name alternatives like 'update_translations' for editing instead of publishing.
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 full burden and adds valuable behavioral context: it discloses the bulk nature (up to 100 keys per request), required/optional fields, and character limits. However, it doesn't mention authentication needs, rate limits, or error handling.
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 clear opening sentence, followed by details in bullet points. It's appropriately sized, though the 'Args:' section could be integrated more seamlessly. Every sentence adds value without 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?
For a mutation tool with no annotations and no output schema, the description covers key aspects like parameters and constraints, but lacks information on return values, error conditions, or project context. Given the complexity, it's adequate but has clear gaps in completeness.
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 fully compensates by detailing the 'keys' parameter structure: it explains it's a list of dictionaries with required 'key' field and optional 'namespace' and 'description' fields, including character limits. This adds essential 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 specific action ('create translation keys in bulk') and resource ('for a project'), distinguishing it from siblings like 'update_translations' or 'get_translations_for_keys'. The verb 'create' and scope 'bulk' provide precise purpose.
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 bulk creation of translation keys, but does not explicitly state when to use this tool versus alternatives like 'duplicate_translation' or 'update_translations'. No exclusions or prerequisites are mentioned, leaving usage context somewhat vague.
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 effectively describes the tool's behavior: it returns a list of dictionaries with specific fields (key, namespace, description, translations) and explains how missing translations are identified (by comparing keys against languages with at least one translation). It does not cover aspects like rate limits, authentication needs, or error handling, but provides sufficient context for basic use.
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 and front-loaded: the first sentence clearly states the purpose, followed by details on functionality and return format. Every sentence adds value—explaining the missing translation logic and output structure—with no redundant or wasted 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 tool's complexity (moderate, involving data retrieval and comparison), no annotations, no output schema, and 0 parameters, the description is largely complete. It explains what the tool does, how it identifies missing translations, and details the return structure. However, it lacks information on error cases or performance considerations, which could be helpful for full contextual understanding.
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 0 parameters with 100% coverage, so the schema fully documents the lack of parameters. The description does not add parameter information, which is unnecessary here. Since there are no parameters, the baseline is 4, as the description need not compensate for any gaps.
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: 'Get a list of translation keys that have missing translations.' It specifies the verb ('Get'), resource ('translation keys'), and scope ('missing translations'), distinguishing it from siblings like 'get_translations_for_keys' (which retrieves translations for given keys) and 'update_translations' (which modifies translations).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by explaining the tool's focus on 'keys that are missing translations in one or more languages' and comparing 'against all languages that have at least one translation in the project,' suggesting it's for identifying gaps in translation coverage. However, it does not explicitly state when to use this tool versus alternatives like 'get_translations_for_keys' or provide exclusions, leaving some ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool 'copies all translations' and indicates it returns a success/failure message, but it lacks details on permissions, rate limits, error handling, or whether the operation is idempotent. The description adds some behavioral context but is incomplete for a mutation 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 well-structured and front-loaded with the core purpose, followed by usage context and detailed parameter explanations. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.
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 (mutation with nested objects) and lack of annotations or output schema, the description does a good job covering purpose, usage, and parameters. However, it could improve by detailing return values beyond 'string message' or addressing potential side effects, leaving some gaps in completeness.
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 fully. It does so by detailing the parameters: 'from_dict' and 'to_dict', each with required 'key' and optional 'namespace' fields, explaining their roles as source and destination. This adds significant meaning beyond the bare schema, fully documenting the two 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 purpose with specific verbs ('duplicate', 'copies') and resources ('translations', 'key/namespace'). It distinguishes itself from siblings like 'create_translation_keys' or 'update_translations' by focusing on copying existing translations rather than creating new ones or modifying them directly.
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 on when to use this tool ('useful for duplicating translations when creating similar keys or reorganizing translations'), but it does not explicitly state when not to use it or name alternatives among siblings. For example, it doesn't contrast with 'create_translation_keys' for new keys or 'update_translations' for modifications.
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 provided, the description carries the full burden of behavioral disclosure. It describes the return format in detail, including fields like 'reviewStatus' and 'lastModifiedAt', which adds useful context beyond basic fetching. However, it lacks information on error handling, rate limits, authentication needs, or whether this is a read-only operation, leaving some behavioral aspects unclear.
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 and front-loaded with the core purpose, followed by detailed parameter and return explanations. Every sentence adds value: the first states the action, the second clarifies scope, and the subsequent sections provide necessary details without redundancy. It efficiently covers key information in a compact format.
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 moderate complexity (2 parameters, no output schema, no annotations), the description is largely complete. It explains the purpose, parameters, and return structure thoroughly. However, it lacks details on error cases or operational constraints (e.g., rate limits), which would enhance completeness for a tool with no annotations.
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 fully compensate. It clearly explains both parameters: 'keys' as a required list of translation keys to fetch, and 'namespace' as an optional filter. The description adds essential meaning beyond the bare schema, specifying that namespace filters translations and defaults to all namespaces if not provided.
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 specific action ('fetches translations') and resource ('for a list of specified keys'), distinguishing it from siblings like 'create_translation_keys' (creation) or 'get_missing_translations' (missing items). It explicitly mentions the scope ('across all languages in the project'), making the purpose unambiguous.
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 on when to use this tool: for fetching translations for specific keys, optionally filtered by namespace. It implies usage versus alternatives by specifying it returns 'all available translations' for given keys, but does not explicitly state when not to use it or name specific sibling alternatives for different scenarios.
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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