eleata e-invoice MCP server
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Each tool has a clearly distinct purpose: error code explanation, format listing, and invoice validation. No overlap or ambiguity.
Naming Consistency5/5All tool names follow a consistent verb_noun pattern: explain_error_code, list_formats, validate_einvoice.
Tool Count5/53 tools is well-scoped for a focused e-invoice validation server, covering the core needs without excess.
Completeness5/5The tool surface covers the full workflow: understanding formats, validating invoices, and interpreting errors. No obvious gaps for the domain.
Average 4.1/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 6 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 MIT License.
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
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the validation behavior, return value structure, and authentication requirement. It does not mention side effects, rate limits, or cost, but for a read-only validation tool this is acceptable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences with no wasted words. It front-loads the core validation action and output, then usage guidance, then authentication. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description adequately explains returns. All parameters are documented, and authentication is covered. It lacks detail on error handling or response format specifics, but overall sufficient for a validation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with good descriptions. The main description adds overall purpose but does not significantly enhance parameter meaning beyond the schema descriptions. The API key is mentioned as an external requirement.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool validates EU electronic invoices against multiple official standards, and lists what it returns (validity and violation details). However, it could better distinguish from sibling tool explain_error_code, which likely serves a complementary role.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly says to use before shipping or transmitting invoices to catch rejections early, and notes the required API key. It does not discuss when not to use or explicitly compare to sibling tools.
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 bears the full burden. It discloses that the operation is a list (read-only) and that no API key is needed, which is valuable. However, it does not mention whether data is cached or any rate limits; for a simple list operation, this is acceptable but not exhaustive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—two sentences with no wasted words. It front-loads the core purpose and then adds the no-API-key detail. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description should explain return values. It states what is listed but not the format (e.g., array of strings) or any pagination. For a simple tool, this is adequate but incomplete; the agent lacks full information about the expected response structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, and the input schema is fully covered. The description adds meaning beyond the schema by specifying that the list includes both current formats and roadmap items, giving context to the output. Baseline for 0 params is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'List' and the resource 'EU e-invoice formats', including both current validation capabilities and roadmap items. It is specific and distinguishes itself from sibling tools (explain_error_code and validate_einvoice) by focusing on listing supported formats.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for discovering supported e-invoice formats and mentions no API key required, but does not explicitly compare to sibling tools or provide when-not-to-use guidance. It lacks explicit context on when to choose this tool over alternatives.
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. It discloses offline operation and no API key requirement, which is valuable. However, it does not mention behavior on unknown error codes or rate limits, which would improve transparency for a read-only tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence. Every word adds value: explains action, gives examples, states offline/no-key. No unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description is nearly complete. It describes the output (plain English, fix, example) and usage context. Minor gap: it doesn't explicitly state that the output is text, but that's implied.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a single parameter. The description adds value by providing concrete examples (00400, BR-DE-21) and clarifying the parameter is for a single code. This goes beyond the schema's dry definition.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: explain a single e-invoice validation error code in plain English with fix and example. It distinguishes itself from siblings (list_formats, validate_einvoice) by focusing on error code explanation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or when-not-to-use guidance. Usage is implied (after validation errors), but no alternatives or exclusions are mentioned. The context from sibling tools makes it understandable but the description does not provide direct guidance.
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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