einvoice-mcp

by makririch

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Server Quality Checklist

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Latest release: v0.1.2
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: convert_format handles format conversion, create_xrechnung and create_zugferd create specific invoice types, extract_data extracts data from invoices, get_format_info provides reference information, and validate_invoice validates invoices. The descriptions reinforce these distinct roles, making tool selection unambiguous.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using snake_case: convert_format, create_xrechnung, create_zugferd, extract_data, get_format_info, and validate_invoice. This uniformity makes the tool set predictable and easy to navigate for an agent.
Tool Count5/5
With 6 tools, the server is well-scoped for e-invoice processing, covering creation, conversion, validation, data extraction, and reference information. Each tool earns its place without bloat, fitting typical expectations for a domain-specific server.
Completeness4/5
The tool set provides strong coverage for e-invoice workflows, including creation (XRechnung and ZUGFeRD), conversion between formats, validation, data extraction, and reference info. A minor gap is the lack of update or delete operations for invoices, but this is reasonable as invoices are typically immutable once created, and agents can work around this by recreating or modifying data externally.
Average 3.1/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
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This repository includes a README.md file.
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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 carries the full burden. It states the tool creates a valid XRechnung XML, implying a write operation, but doesn't disclose behavioral traits such as error handling, validation steps, or output format details. This is inadequate for a complex tool with 15 parameters.
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 and front-loaded, stating the core purpose in one bilingual sentence. It avoids redundancy and wastes no words, though it could be slightly more structured by separating key points.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex tool with 15 parameters, nested objects, and no annotations or output schema, the description is incomplete. It lacks details on behavioral aspects, error handling, and output expectations, leaving gaps that could hinder effective tool selection and invocation.
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 100%, so the schema already documents all parameters thoroughly. The description adds no additional parameter semantics beyond what the schema provides, such as explaining relationships between fields or usage examples. Baseline 3 is appropriate when schema does the heavy lifting.
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 creates an XRechnung XML from structured data, specifying the format (UBL 2.1) and standards (EN 16931 + XRechnung 3.0.2). It distinguishes from siblings like 'create_zugferd' by focusing on XRechnung, though it doesn't explicitly compare them.
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 explicit guidance on when to use this tool versus alternatives like 'create_zugferd' or 'convert_format'. The description mentions conformance to specific standards, which implies usage for XRechnung-compliant invoices, but lacks clear when/when-not scenarios or prerequisites.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool extracts data but doesn't describe what happens if the XML is invalid, what structured data is returned (e.g., fields like invoice number, date), or any performance or error-handling traits. This is inadequate for a tool that processes XML input without output schema details.
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 brief and front-loaded, with the core purpose stated first in both German and English. The bilingual repetition is slightly redundant but doesn't significantly detract from efficiency. It avoids unnecessary elaboration, making it easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (processing XML for data extraction), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what structured data is extracted, how errors are handled, or the format of the output, leaving critical gaps for the agent to understand the tool's behavior and results.
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 description coverage is 100%, so the schema already documents both parameters ('xml' as a string and 'base64' as a Base64-encoded XML file). The description adds no additional meaning beyond implying these are alternative input methods for e-invoice XML, which is already clear from the schema. This meets the baseline for high schema coverage.
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: 'Extracts structured data from an e-invoice XML.' It specifies the verb ('extracts'), resource ('structured data'), and source format ('e-invoice XML'), though it doesn't explicitly differentiate from sibling tools like 'validate_invoice' or 'convert_format'. The bilingual phrasing adds clarity but doesn't enhance 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?
The description provides no guidance on when to use this tool versus alternatives. It mentions the input formats (UBL-XML or CII-XML) but doesn't specify scenarios where extraction is needed over validation or conversion, nor does it mention prerequisites or exclusions. This leaves the agent with minimal context for tool selection.
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 of behavioral disclosure. It mentions validation actions but lacks details on permissions, rate limits, error handling, or output format. For a validation tool with zero annotation coverage, this is insufficient, as it doesn't describe what happens during or after validation beyond the basic purpose.
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 and front-loaded, stating the core purpose in the first sentence and adding a brief English translation. Both sentences earn their place by clarifying the tool's function, though it could be slightly more structured to highlight key aspects like validation levels. No wasted words, but minor improvements in organization are possible.
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 (3 parameters, no output schema, no annotations), the description is minimally adequate. It covers the purpose but lacks context on usage, behavioral traits, and output expectations. With 100% schema coverage, it compensates partially, but for a validation tool without annotations or output schema, more completeness is needed to guide an AI agent effectively.
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 100%, so the schema already documents all parameters (xml, base64, level) with descriptions and enum values. The description adds no additional parameter semantics beyond what the schema provides, such as explaining trade-offs between 'xml' and 'base64' inputs or elaborating on 'level' choices. Baseline 3 is appropriate as the schema does the heavy lifting.
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: validating e-invoice XML against XRechnung/ZUGFeRD standards with syntax, mandatory fields, and German business rules. It uses specific verbs ('prüft', 'validates') and identifies the resource (XML). However, it doesn't explicitly differentiate from sibling tools like 'get_format_info' or 'extract_data', which might also involve XML inspection.
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 doesn't mention sibling tools like 'create_xrechnung' or 'convert_format', nor does it specify prerequisites or contexts for validation. Usage is implied but not explicitly stated, leaving gaps for an AI agent to determine appropriateness.
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?
With no annotations provided, the description carries full burden for behavioral disclosure. While it states the tool creates XML for embedding into PDF/A-3, it doesn't mention whether this is a pure generation tool (no side effects), what permissions might be needed, error handling, or performance characteristics. The description is minimal and lacks important behavioral context for a creation tool.
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 efficiently structured in two sentences that convey the core functionality and output format. While it could be slightly more detailed about behavioral aspects, there's no wasted language or redundancy. The information is front-loaded with the primary purpose stated immediately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex invoice creation tool with 13 parameters, nested objects, and no output schema or annotations, the description is insufficient. It doesn't explain what the tool returns (beyond mentioning 'CII XML'), error conditions, validation performed, or how the generated XML should be used with PDF/A-3. The agent lacks crucial context for proper tool invocation.
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 100%, providing comprehensive parameter documentation. The description adds no parameter-specific information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description, which applies here.
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 ('Erstellt', 'Creates') and resources ('ZUGFeRD 2.3 CII XML', 'Factur-X', 'e-invoice XML'), and distinguishes it from siblings by specifying the exact format (ZUGFeRD 2.3 CII) and use case (embedding into PDF/A-3). It explicitly mentions the output format and application context.
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 like 'create_xrechnung' or 'convert_format'. It doesn't mention prerequisites, constraints, or typical scenarios for choosing ZUGFeRD over other invoice formats, leaving the agent without contextual usage information.
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 of behavioral disclosure. It states the tool returns information, implying it's a read-only operation, but doesn't mention potential side effects, error handling, rate limits, or authentication needs. This is a significant gap for a tool with no 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 concise and front-loaded, with two sentences that efficiently convey the tool's purpose and utility. The first sentence lists the key information returned, and the second clarifies its use case. There's no unnecessary repetition or fluff.
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 (2 parameters with enums) and lack of annotations and output schema, the description is somewhat complete but has gaps. It covers the purpose and general use case but omits behavioral details like response format, error conditions, or how the returned information is structured, which is important for a reference 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?
The schema description coverage is 100%, with clear enum descriptions for both parameters. The description adds minimal value beyond the schema by listing topics like 'required_fields' and 'tax_categories', but doesn't provide additional context on parameter interactions or usage examples. Baseline 3 is appropriate given the high schema coverage.
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: it returns information about e-invoicing formats, required fields, tax categories, unit codes, and legal deadlines. It specifies the resource (e-invoicing formats) and the type of information returned, though it doesn't explicitly differentiate from sibling tools like 'validate_invoice' or 'extract_data' beyond being a reference tool.
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 as a reference for creating valid invoices, suggesting it should be used when needing format details. However, it lacks explicit guidance on when to use this tool versus alternatives like 'validate_invoice' or 'create_xrechnung', and doesn't specify prerequisites or exclusions.
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?
With no annotations provided, the description carries full burden but provides minimal behavioral information. It mentions what formats can be converted but doesn't disclose important traits like whether this is a read-only operation, potential side effects, error handling, performance characteristics, or authentication requirements. The description doesn't contradict annotations (none exist), but fails to provide adequate 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?
The description is extremely concise with zero wasted words. It's front-loaded with the core purpose in the first sentence, followed by specific conversion directions. The bilingual presentation (German/English) is efficient for clarity. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (format conversion with multiple directions), no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns, error conditions, input format requirements beyond basic mentions, or how validation works. For a data transformation tool with 3 parameters, this leaves significant gaps in understanding.
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?
With 100% schema description coverage, the baseline is 3. The description adds some value by listing all six conversion directions, which helps understand the 'direction' parameter's enum values, but doesn't provide additional semantic context beyond what's already in the schema descriptions for 'input' and 'validate_output' 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 ('konvertiert', 'converts') and resources ('E-Rechnungen', 'e-invoices'), and explicitly lists all six conversion directions. It distinguishes itself from sibling tools by focusing on format conversion rather than creation, extraction, validation, or information retrieval.
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 about when to use this tool (converting between UBL, CII, and JSON formats), but doesn't explicitly state when NOT to use it or mention alternatives. It doesn't compare with sibling tools like 'get_format_info' or 'validate_invoice' that might be relevant in some 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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