graph-mcp-java-gen

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
The tools are mostly distinct: metadata retrieval, graph search, three variations of Java test generation, and validation. The three generate_java_test* tools could be confused, but their descriptions clearly differentiate by input type (structured, bounded NL, free-form NL with review). Minor overlap exists but descriptions resolve ambiguity.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case: get_fixture_metadata, search_graph, generate_java_test, generate_java_test_from_intent, generate_java_test_nlp, validate_java_source. The naming is predictable and uniform throughout the set.
Tool Count5/5
With 6 tools, the server is well-scoped for its purpose of generating and validating Java tests from a synthetic graph. Each tool serves a clear role in the workflow, and the count feels appropriate—not too few to be inadequate, not too many to be unwieldy.
Completeness5/5
The tool surface covers the full lifecycle: metadata retrieval, graph search for grounding, three generation pathways (including NL-based variants), and validation. There are no obvious missing operations for the stated purpose; the server provides a complete pipeline from context gathering to output validation.
Average 2.8/5 across 6 of 6 tools scored. Lowest: 1.3/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 10 commits in the last 12 weeks
- No stable releases found
- 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

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It fails to disclose side effects (e.g., creating a test file), permissions, reversibility, or any behavioral traits beyond a generic 'Generate'. The vague conditions do not help.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, so it is concise, but it sacrifices substance. It is not front-loaded with a clear action or key information. The vague wording wastes the reader's time and fails to convey the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 5 parameters, 4 required, and an output schema, but the description ignores all of this. It provides no context about expected inputs, outputs, or constraints. The description is wholly inadequate for an agent to use this tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, yet the description mentions none of the five parameters. It offers no hints about class_name, package_name, module_name, config_path, or version. The description adds no value beyond the schema's bare property names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Generate Java' but doesn't specify it generates Java tests, despite the tool name. The conditions ('graph grounding, syntax, contract, and safety pass') are vague and undefined. It doesn't differentiate from sibling tools like generate_java_test_from_intent or generate_java_test_nlp.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. The phrase 'only when...' implies preconditions but doesn't explain what those preconditions mean or how to check them. No mention of alternative tools or contexts.
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 reveals only that 'all gates' are enforced but never states what these gates are, whether generation is a read-only operation, what side effects or failure modes exist, or how output is produced. For an unannotated generation tool, this is a significant transparency gap.
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 single sentence is efficient and front-loaded, with no wasted words. However, the terseness verges on under-specification — it compresses too much meaning into unexpanded terms like 'gates' and 'bounded,' so the brevity comes at the cost of clarity.
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?
Although an output schema exists (which removes the need to explain return values), the tool is complex — natural-language-to-Java generation — with no annotations. The description omits how gates behave, which sibling to prefer, and what constitutes a valid request, so an agent lacks enough information to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the schema gives only the field name 'request' with no description. The description adds a partial hint that the request must be a 'bounded natural-language request', but provides no format, length constraints, language expectations, or examples. With coverage this low, the description was required to compensate and does not.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the general action — parse a bounded natural-language request and generate Java — but omits 'test' even though the tool name says 'generate_java_test_from_intent', leaving the output artifact slightly ambiguous ('generate Java'). The phrase 'enforce all gates' is unexplained jargon. While it gestures at the input mode ('bounded natural-language request'), it does not differentiate against sibling tools generate_java_test and generate_java_test_nlp.
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 given on when to use this tool versus the sibling tools generate_java_test and generate_java_test_nlp, which appear to be competing intent-to-Java generators. An agent cannot determine the selection criteria (e.g., what 'bounded' means, or which tool handles free-form vs constrained intents). No exclusions or alternatives are named.
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 the validation gates (syntax, graph, contract, safety) but does not state whether the operation is read-only, has side effects, or requires specific permissions. It also does not explain the term 'bounded' or describe failure behavior. For a validation tool, read-only is implied but never stated.
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 extremely concise—a single sentence that leads with the verb and core purpose. There is no fluff or redundant phrasing. It earns a high score for efficiency, though it might slightly benefit from a bit more detail.
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 (6 parameters, 5 required) and no annotation support, the description is far too brief. It does not explain what 'bounded' means, what the four gate types entail, or what the output schema represents. While an output schema exists, the description still lacks essential context for an agent to call this tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds no information about any of the six parameters. It does not explain what 'source', 'class_name', 'config_path', etc., mean or how they relate to each other. The parameter names are somewhat self-explanatory, but the description offers zero elaboration on constraints or formats.
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: 'Validate bounded Java source against syntax, graph, contract, and safety gates.' It uses a specific verb ('validate') and a precise resource ('bounded Java source'), and it distinguishes this tool from its siblings (which mainly generate tests) by focusing on validation.
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?
There is no guidance on when to use this tool versus the alternatives. It does not mention any prerequisites, conditions, or scenarios where it would be preferred over the sibling tools (e.g., generate_java_test). Users must infer usage from the name alone.
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. The description only states it 'searches' the graph, implying a read‑only operation, but does not explicitly confirm that, nor does it mention any side effects, permissions, rate limits, or how results are returned (e.g., sorted, paginated). The lack of explicit behavioral info and absence of annotations means an agent cannot assess safety or performance expectations.
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 a single, well‑formed sentence that is front‑loaded with the action and resource. There is no redundancy or filler language. Every word contributes to the core meaning, and it is short enough to be read quickly without sacrificing clarity. This is an appropriate level of conciseness for the tool.
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 that an output schema exists, the description is not required to explain return values, but it must still cover the operational context. The description lacks details about when to use the tool, how parameters interrelate, any search behavior (e.g., prefix matching, case sensitivity), or potential edge cases. With three parameters and zero schema descriptions, the definition is incomplete for an agent to use correctly without further investigation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must clarify the meaning of each parameter. It partially does for 'query' by indicating it can be a class, qualified name, or method, but it says nothing about 'limit' or 'version'. These parameters are left completely unexplained, and no defaults or expected formats are mentioned. The description only adds minimal semantic value for one of the three 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 states a specific verb ('Search') and a precise resource ('the bounded synthetic graph'), and further qualifies search criteria ('by class, qualified name, or method'). This makes the tool's function unambiguous and clearly distinguishes it from sibling tools, which are all about generating or validating Java sources rather than searching a graph. The phrase 'bounded' also hints at a limited dataset scope, adding useful 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 its siblings or any other alternative. It does not state conditions like 'use this when you need to locate a fixture by name' or mention any exclusion criteria. An agent must infer from the tool name and description alone, which is insufficient for routing decisions among tools with similar purposes.
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?
Annotations are absent, so the description must carry the behavioral burden. It discloses that it is a multi-agent pipeline involving an LLM, that it requires an API key, and that it includes a review step. However, it does not mention potential rate limits, costs, failure modes when the API key is invalid, or whether the operation is read-only or has side effects. The disclosure of the API key need is a plus, but the overall transparency is only moderate.
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, containing three clear sentences that front-load the core purpose. It avoids fluff and directly states the pipeline stages and the environment requirement. The structure is logical and efficient, with the most important information (purpose) placed first.
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 single parameter and the presence of an output schema, the description is reasonably complete for an agent to call the tool. It covers the input type (free-form NL), the required environment variable, and the overall process. However, it does not specify the exact output format (which is partially covered by the output schema) or any error-handling behavior, but these are minor gaps given the tool's simplicity.
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 a single parameter and 0% schema description coverage, the description must clarify the parameter's meaning. It does so implicitly by stating the pipeline parses 'free-form NL', implying that 'request' is the natural language input. This is minimally sufficient, but no example, format, or constraints are given, leaving room for ambiguity about what constitutes valid input.
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 a specific purpose: a multi-agent pipeline that parses free-form natural language, generates Java, and reviews it. The verb 'generates' and resource 'Java test' are explicit. While it doesn't explicitly contrast with sibling tools like generate_java_test or generate_java_test_from_intent, the focus on 'free-form NL' differentiates it from intent-based alternatives, so an agent can infer its role.
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?
There is no explicit guidance on when to use this tool versus its siblings. The only context is the requirement for OPENAI_API_KEY, which is a prerequisite, not a usage condition. No mention of alternatives or exclusion criteria is provided, so an agent must infer that it is intended for free-form natural language input.
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 states the tool 'Return' information, implying a read-only operation, but does not explicitly disclose side-effect-free behavior, auth requirements, or failure modes. For a simple metadata getter, this is adequate but not thorough.
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 a single sentence with no wasted words. It is front-loaded with the action and object, and every word contributes to meaning. Excellent conciseness.
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?
The description provides enough information for an agent to understand the tool's purpose and what it returns. Given the zero-parameter schema and the presence of an output schema, the description is complete for the tool's low complexity, though it doesn't elaborate on what 'provenance' entails.
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 takes zero parameters, so the baseline is 4. The description adds no parameter-specific context because none is needed. It correctly focuses on what the tool returns rather than parameter details.
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 states a specific verb (Return) and resource (provenance and version information for the active graph fixture). It clearly distinguishes from sibling tools like search_graph and generate_java_test, which serve entirely different purposes. The purpose is 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 implies use for retrieving metadata about the active graph fixture, which is clear given the tool name and siblings. However, it does not explicitly state when not to use it or mention alternatives. The context is clear enough that an agent would not confuse it with the search or test generation 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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