jPOS MCP Server

Server Quality Checklist

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Latest release: v0.1.1
Disambiguation5/5
Each tool has a clearly distinct purpose covering documentation search, health status, ISO 8583 MTI decoding, field lookup, packager generation, descriptor validation, and message building. No overlap or ambiguity exists.
Naming Consistency3/5
Names use mixed conventions: some start with 'jpos_', some with 'iso_', one with 'msg_', and one is just 'health'. While all are snake_case and mostly verb_noun, the lack of a uniform prefix or structure reduces consistency.
Tool Count5/5
With 7 tools, the server covers essential areas of jPOS configuration and ISO 8583 operations without being overwhelming. The scope is well-scoped for development and validation tasks.
Completeness4/5
The set covers documentation search, health, MTI decoding, field specs, packager generation, descriptor validation, and message validation. Missing encoding/decoding of raw messages is a minor gap but the core workflow is supported.
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
- 0 commits in the last 12 weeks
- Last stable release on April 12, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
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

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 disclose key behaviors (rejects real PANs for safety via Luhn, checks MTI, mandatory fields, length). However, it does not describe what happens on failure or output format, which limits transparency.
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) and front-loaded with the main purpose. Each sentence adds valuable detail without redundancy.
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 existence of an output schema, the description adequately covers the tool's validation criteria. It could elaborate on error behavior, but overall it provides sufficient context for a validation 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?
Schema coverage is 100%, so baseline is 3. The description does not add new parameter-specific meaning beyond the schema example; it explains the validation logic but not the parameter structure itself.
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 an ISO 8583 message field dictionary, with specific checks (MTI, mandatory fields, length, PANs). This distinguishes it from siblings like iso_decode_mti and iso_lookup_field, which have distinct purposes.
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 when to use the tool (for validation of field dictionaries) but does not explicitly state when not to use it or suggest alternatives. Context is clear but lacks exclusionary guidance.
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 description carries the burden. It describes validation checks but does not explicitly state read-only nature, error handling, or performance characteristics. Adds value beyond 'validate' but not fully comprehensive.
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 efficient sentences: first states purpose, second lists checks. No fluff, every sentence adds value. 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?
With one parameter, high schema coverage, and an output schema, the description is sufficient for an agent to understand and invoke the tool. Lacks usage guidelines but otherwise 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 coverage is 100% with a clear parameter description. The tool description reinforces the XML content type and checks but adds minimal additional semantics beyond what the schema already provides.
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 tool validates a jPOS Q2 deploy descriptor XML file, listing specific checks (well-formed XML, required attributes, rules, typos). This distinguishes it from sibling tools like docs_search_jpos or health.
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?
While no explicit when-to-use or alternatives, the purpose is self-explanatory and siblings are unrelated, making context clear. Lacks explicit 'when not to use' guidance.
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 full burden. It discloses that the tool decodes into four components and provides special behavior for request messages. However, it does not mention auth needs, rate limits, or side effects, though as a decode operation these are minimal.
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, front-loaded with the main function, then a useful additional detail. 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?
For a simple decode tool with one parameter and an output schema (not provided but assumed), the description explains the decoding result and request-response behavior. It does not detail output format, but the existence of an output schema mitigates that.
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 coverage is 100%, with the schema already describing the mti parameter as a 4-digit ISO 8583 MTI string with pattern and length constraints. The tool description repeats '4-digit MTI' but adds no new semantic information beyond what the schema provides.
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 decodes a 4-digit MTI into version, message class, function, and origin. The verb 'Decode' and resource 'MTI' are specific, and the tool distinguishes itself from siblings like iso_lookup_field.
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 a specific usage hint: for request messages (function digit = 0), it returns the expected response MTI. This helps the agent understand a special case but does not explicitly state when to use this tool over alternatives or when not to use it.
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 provided, so description carries full burden. It comprehensively discloses the retrieval pipeline (query expansion, embedding, reranking), score calibration thresholds, and the rationale for returning raw chunks instead of summaries.
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 front-loaded purpose, followed by return format, score calibration, pipeline, and rationale. Each sentence provides useful information, though the pipeline detail may be slightly verbose.
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 simplicity (one parameter, no output schema shown), the description covers query processing, scoring, and return format. It could mention potential errors or rate limits, but overall is fairly complete.
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 100% with a clear parameter description. The tool description adds value by explaining how the query is expanded and used in the pipeline, going beyond the schema's basic description.
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: 'Search jPOS documentation using hybrid semantic + keyword search.' It specifies the resource (jPOS documentation) and action (search), and is distinct from sibling tools like iso_decode_mti or msg_build_message.
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 searching documentation but does not explicitly state when to use this tool versus alternatives or provide any when-not guidance. No mention of other tools or conditions.
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. It lists all returned fields (name, format, type, etc.), disclosing output content. Although it does not explicitly state read-only nature, the lookup behavior is obvious and no side effects are implied.
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 (two sentences) and well-structured: first sentence states purpose, second details return fields, third gives usage advice. Every sentence adds value with no redundancy.
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 simplicity, full schema coverage, and presence of an output schema, the description is complete. It covers what, how, and when to use, satisfying all informational needs for an agent.
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 coverage is 100%, and the parameter description matches the schema. The description adds no new meaning beyond what the schema already provides, so baseline score of 3 is appropriate.
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 'Return the complete ISO 8583 specification for a data element number', using a specific verb and resource. It distinguishes itself from siblings like iso_decode_mti or msg_build_message by focusing on field specification lookup.
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 'Use this before writing any GenericPackager XML', providing clear context for when to use the tool. However, it does not mention when not to use or suggest alternatives, slightly reducing the score.
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 explicitly states the tool returns data (version, counts) implying no side effects. It could mention idempotency or safety, but for a health check, this is sufficient.
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?
A single sentence with no wasted words. The main purpose is front-loaded. Every word adds value.
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 parameters and existence of an output schema, the description adequately covers the return fields. It is likely sufficient for an agent to understand what the tool provides.
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 tool has zero parameters, so the schema coverage is 100% trivially. The description does not need to explain parameters. Baseline for 0 params is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns server health status including specific fields (version, tool count, ChromaDB chunk count). This distinguishes it from sibling tools which deal with ISO messages and document search.
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?
While no explicit when-to-use guidance is given, the tool name and description make its purpose obvious. Since siblings are all unrelated, no alternative is needed. The context is clear.
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 it uses a JSON file for accurate class names, is deterministic, and always includes mandatory fields. With no annotations, it carries the burden well, though it could mention error handling or permissions.
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 paragraphs, front-loaded with purpose, every sentence informative. No unnecessary words.
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 single parameter with full schema coverage and presence of output schema, description covers input, process, and output purpose adequately.
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 has 100% coverage with a good description and example. Tool description adds value by explaining how the parameter is used (parse field numbers, optionally encoding type), going beyond 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?
Clearly states it generates a jPOS GenericPackager XML configuration, explains the process (parsing fields, looking up classes, assembling XML), and specifies it always includes fields 0 and 1. This distinguishes it from siblings like iso_decode_mti or iso_lookup_field.
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 context on why this tool is reliable (uses iso_fields.json ground truth vs LLM hallucinations), implying when to use it. However, lacks explicit exclusions or direct comparisons to sibling tools.
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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