anonymize-mcp

Server Quality Checklist

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Latest release: v0.10.2
Disambiguation5/5
Each tool has a unique, well-defined purpose: morphological analysis, anonymization, readability checking, text correction, entity extraction, and translation. There is no overlap in functionality, and descriptions clearly differentiate them.
Naming Consistency4/5
Most tools follow a verb_noun pattern (analyze_morphology, check_readability, correct_text, extract_entities, translate_text), while 'anonymize' is a standalone verb. This minor inconsistency does not hinder understanding.
Tool Count5/5
With 6 tools, the server covers essential NLP tasks for Czech legal texts without being over- or under-scoped. Each tool earns its place.
Completeness4/5
The toolset provides a comprehensive pipeline for processing legal texts (analysis, correction, anonymization, translation, entity extraction, readability). Minor gaps like summarization exist, but core workflows are well covered.
Average 4.4/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 33 commits in the last 12 weeks
- Last stable release on August 11, 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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}
```
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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?
The description discloses behavioral traits: it mentions PONK is designed for official communication, notes the highlight HTML is large (100+ KB) for bandwidth consideration, and explains the optional feature sets. No annotations exist, so the description carries the burden; it is fairly transparent about what is returned and the cost of certain options.
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 numbered lists and bullet points, front-loading the purpose. It is reasonably concise for the complexity, though slightly verbose with repeated enumeration of feature sets.
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 complexity (6 parameters, 1 required, and a rich output schema), the description covers the returned fields and optional components. It is complete enough for an agent to understand outputs, though it could mention that the output schema exists but doesn't need to detail it.
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%, but the description compensates by explaining each parameter's purpose, default values, and the effect of include flags. It also warns about the highlight HTML size. This adds significant meaning beyond the raw 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 'Analyzuje čitelnost českého textu pomocí PONK' and lists four specific feature sets (overall metrics, grammatical rules, lexical surprise, speech acts). It distinguishes itself from sibling tools like analyze_morphology, anonymize, etc., by focusing on readability analysis.
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 explains what the tool does but does not provide explicit guidance on when to use it versus siblings or when not to use it. Usage is implied through the purpose, but no when/when-not or alternative recommendations are given.
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 of behavioral disclosure. It reveals the tool is CZ-only, models are from 2013, and accuracy may be limited for proper names and new words. It also lists return fields (corrected, model, mode, changed), providing transparency about output.
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 with sections for use cases, args, and returns. It is front-loaded with the main purpose and every sentence adds value without waste. The length is appropriate for the complexity.
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 2-parameter tool with no annotations, the description covers purpose, parameters, modes, return values, and limitations. It lacks details on error handling or idempotency, but overall it is sufficiently complete for an AI agent to understand and invoke the tool correctly.
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 has 0% description coverage; the description compensates for the 'mode' parameter by detailing each enum value's purpose and examples. However, for 'text', it only states 'Vstupní český text' without additional semantics, which is minimal but clear given the tool name.
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 corrects Czech text using Korektor for spelling or diacritics. It distinguishes from sibling tools by listing specific modes (spellcheck, diacritics, strip) and use cases relevant to legal-tech, with examples like OCR output enhancement.
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 explains each mode's purpose and when to use them (e.g., diacritics for OCR outputs, strip for URL slugs). It also cautions that the tool is CZ-only and notes model limitations from 2013, providing clear context for proper usage.
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?
No annotations provided, so description carries full burden. It discloses EN-pivot behavior (two calls, warning, pivot flag), document mode constraints, and preservation of proper names. The return structure is described. Mutation aspect is not explicit but translation is inherently non-destructive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is verbose (over 300 words) with extensive lists and examples. While well-structured with Markdown and headings, it could be shortened without losing essential information. Some detail is redundant 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 complexity of language pairs and pivot logic, the description covers all necessary behavioral and constraint details. Output schema exists, so return values are documented separately. Missing an explicit example call, but overall complete.
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?
Input schema has 0% description coverage, so description must compensate. It explains each parameter: 'text' (UTF-8), 'src' (default cs), 'tgt' (default en), and 'document_mode' (only for cs↔en, preserves structure). 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 translates text using Charles Translator, specifies 8 languages and 17 direct pairs, and distinguishes it from sibling tools like analyze_morphology or anonymize. The verb 'přeloží' (translate) and resource are explicit.
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 extensive usage guidance: supported languages, direct vs. pivot pairs, document mode limitations, and key pairs for legal-tech. It implicitly advises when to avoid pivot (no doc-mode) and mentions missing SK↔CZ. However, it doesn't explicitly contrast with sibling tools, though purpose is distinct enough.
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?
No annotations are provided, so the description carries full burden. It discloses that include_xml and include_vertical cause extra API calls, and fix_romance generates warnings. It does not mention rate limits or auth, but the behavioral traits are adequately described.
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 an overview, parameter details, and return values. It is informative without being overly verbose, though a slightly more streamlined presentation could improve conciseness.
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 complexity (5 parameters, 1 required, output schema present), the description covers all parameters and return values comprehensively. It also notes extra API calls for certain options, ensuring the agent has sufficient context.
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?
With 0% schema coverage, the description fully explains each parameter: text, model (with values and defaults), fix_romance (function and default), include_xml, include_vertical. It provides concrete details beyond the schema, such as model name examples and the effect of fix_romance.
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 recognizes named entities using NameTag 3, supporting Czech with a rich tagset and over 30 other languages with a multilingual model. It distinguishes itself from sibling tools like anonymize or correct_text by focusing specifically on entity extraction.
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 different model options (auto, czech, multilingual) and mentions fix_romance for specific languages. However, it does not explicitly state when not to use this tool or compare it to alternatives, leaving some ambiguity.
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?
No annotations present, so description fully handles transparency. Discloses UDPipe 2 usage, 961 model support, auto-detect language list, and behavior of optional parameters (include_parse, include_ranges) with practical examples.
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?
Well-structured with bullet points and clear sections. Slightly lengthy due to language list, but overall concise and front-loaded with core purpose.
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 presence of output schema, description need not detail return values. Covers input, use cases, and optional outputs adequately. Mentions output fields (sentences, model, token_count, etc.) for agent to infer structure.
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 0% requires description to explain parameters. Description explains 'text', 'model' (with defaults and examples), and both boolean flags with usage context. Lacks constraints (e.g., text length), but otherwise sufficient.
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 tool performs tokenization, lemmatization, and POS tagging using UDPipe 2. Lists specific output fields (lemma, UPOS, morphological features, optional parse, ranges). Distinguishes from sibling tools like extract_entities and translate_text by focusing on morphological 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?
Provides explicit use cases (fulltext search, POS filtering, passive detection, multilingual documents) and mentions language auto-detection. Does not explicitly state when not to use, but the use cases are clear and differentiate from siblings.
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?
No annotations are provided, so the description carries full burden. It details the 8-step pipeline, each parameter's effect (e.g., placeholder_mode for reproducibility, stop_list_filter for false positives), and return structure. This is highly transparent about the tool's behavior and 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with numbered steps and bullet points, but it is verbose. Every sentence provides value, and it is front-loaded with the tool's purpose. However, it could be slightly more concise without losing clarity.
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 complexity (8 parameters, multi-step pipeline, return types), the description is remarkably complete. It explains pipeline stages, parameter interactions, output fields, and even version-specific features. The output schema is described in the Returns section, fulfilling 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%, but the description compensates by explaining each parameter (text, output, keep_mapping, etc.) in plain language, including defaults and behavioral impact. This adds significant meaning 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 it is a 'Production-grade pseudonymizace českých právních textů' (pseudonymization of Czech legal texts). It provides a specific verb (pseudonymize), resource (Czech legal texts), and detailed pipeline. This distinguishes it from sibling tools like analyze_morphology or translate_text.
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 the pipeline and parameter behaviors, implying usage for Czech legal text anonymization. It mentions caveats like turning off keep_mapping if text leaves confidential environment. However, it does not explicitly state when not to use this tool or provide alternatives among siblings.
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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