swagger-json-mcp

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool targets a distinct resource and action: listing swaggers, getting overviews, retrieving schemas, retrieving API info, and searching APIs or schemas. No overlap in purpose; agents can easily select the right tool.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using snake_case (list_*, get_*, search_*). The nouns are clear and descriptive, with only minor pluralization inconsistencies that do not affect predictability.
Tool Count5/5
Six tools is well-scoped for a Swagger/OpenAPI inspection server. Each tool covers a distinct aspect of reading and exploring API definitions without being excessive or minimal.
Completeness5/5
The tool surface provides a complete read-only workflow: list available Swagger files, get overview statistics, retrieve detailed schema definitions with $ref resolution, get full API info, and search for APIs/schemas. No obvious gaps for a browsing/inspection use case.
Average 3.5/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 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
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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?
With no annotations provided, the description must carry the full burden of behavioral disclosure. It only says 'overview and statistics' but does not specify what data is returned, whether it is read-only, if there are pagination or size limits, or what the output structure looks like. This lack of detail is a notable gap.
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, concise sentence that is front-loaded with the verb '獲取' and clearly identifies the resource. There is no unnecessary wording or repetition.
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?
The tool is simple (1 parameter, no output schema), but the description could be more complete by elaborating on what 'overview and statistics' includes. Without an output schema, the agent must infer return values, and the vague phrasing leaves room for ambiguity. It is minimally adequate but not fully 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?
The input schema already describes the only parameter (swaggerName) with 100% coverage, so the baseline is 3. The description does not add any additional meaning to the parameter beyond what the schema provides, which is acceptable for a single, self-explanatory parameter.
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 action ('獲取' - get) and resource ('Swagger 檔案的整體概覽和統計資訊' - overall overview and statistics of the Swagger file), which is specific enough to differentiate from sibling tools like get_schema or get_api_info. However, it doesn't explicitly contrast with those siblings, so it falls short of a 5.
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 such as list_swaggers, get_schema, or search_apis. There is no mention of prerequisites (e.g., needing a Swagger file name from list_swaggers) or scenarios where this tool is preferred.
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 only mentions automatic $ref resolution, but omits critical behaviors such as how maxDepth limits resolution depth, how includeCircular handles circular references, and what the return format looks like. This is a significant gap given the tool's complexity.
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-structured sentence that front-loads the core purpose and adds the key feature of $ref resolution. No unnecessary words, and it is appropriately sized for the tool's purpose.
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?
Despite a fully documented schema, the tool lacks an output schema and annotations, and the description is too brief to explain how parameters like maxDepth and includeCircular affect the result or what the response structure is. Sibling tools are not addressed, and the behavior around circular references and depth is left unclear, making the description incomplete for an agent to use the tool 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 input schema already explains all four parameters with descriptions. The description adds no additional parameter semantics, but the baseline of 3 is appropriate since the schema fully documents the parameters and their defaults.
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 retrieves the complete schema definition and automatically resolves all $ref references, which differentiates it from sibling tools like search_schemas or get_swagger_overview. The verb '獲取' and resource 'schema' are explicit, and the $ref resolution detail adds specificity beyond the tool name.
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 when a full schema definition with resolved references is needed, but it provides no explicit guidance on when to use this tool versus alternatives like search_schemas or get_swagger_overview. There are no exclusions or alternative recommendations, leaving the usage context to be inferred.
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?
With no annotations, the description carries the burden of explaining behavior. It does add one useful behavioral detail—that all related schemas are automatically parsed—which goes beyond a simple 'get' statement. However, it does not disclose what 'complete info' includes, whether authentication is needed, or any potential performance implications.
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 that front-loads the action and resource, with no wasted words. It effectively communicates the core function and the schema-parsing behavior.
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?
The description is adequate for a simple read-only tool but omits details about the return format, how to discover valid swaggerName values, or any constraints. Given there is no output schema and no annotations, a more complete description would be beneficial, but it still manages to be minimally sufficient.
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 input schema provides full descriptions for all three parameters (swaggerName, path, method), achieving 100% coverage. The tool description adds no additional parameter semantics, so the baseline score of 3 is appropriate; the schema already does the heavy lifting.
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 identifies the tool's purpose: '獲取特定 API 的完整資訊' (get complete info for a specific API) and adds '自動解析所有相關 schemas' (automatically parse all related schemas), which distinguishes it from sibling tools like get_schema or search_apis. The verb and resource are specific and unambiguous.
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 when-to-use guidance is provided. The description does not mention alternatives, prerequisites, or contexts where this tool should be preferred over siblings. The user is left to infer that it is for specific API lookups, but no explicit usage direction is given.
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. It only states the basic action, but does not disclose output format, potential size of the list, or any side effects. 'List' implies read-only, but no further context is offered.
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, clear sentence with no wasted words. It conveys exactly what the tool does in an appropriately concise manner.
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?
The tool is simple with no parameters and no output schema, so the description is minimally viable. However, it does not clarify how the returned list is structured or how it relates to siblings like get_swagger_overview, leaving some ambiguity for a complete understanding.
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 accepts zero parameters, so the baseline of 4 applies. The description correctly implies no parameters are needed, and the empty input schema confirms this. No additional parameter semantics are required.
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 action ('list') and the resource ('all available Swagger files'), distinguishing it from siblings like get_swagger_overview and search_apis which are more specific or filter-based.
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 search_apis. The description implies usage for listing all swaggers, but does not clarify scenarios where siblings would be more appropriate.
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?
With no annotations, the description carries the full burden. It discloses some behavioral traits: fuzzy search and multiple filter conditions. However, it does not mention whether the operation is read-only, what the return structure looks like, or whether there are pagination or performance characteristics.
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 front-loaded sentence that immediately states the action and resource. There is no fluff or redundant information.
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?
For a search tool with 5 parameters fully described in the schema, the description is adequate but incomplete. It lacks details about the return format (though no output schema exists), and it does not mention how search results are ordered or what fields are included.
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 the baseline is 3. The description adds little beyond the schema: 'fuzzy search' hints at query matching semantics, but the filter parameters (tag, method, swaggerName) are already well-documented in the 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's purpose: '搜索 API endpoints' (search API endpoints). It distinguishes from sibling search_schemas by targeting endpoints rather than schemas, and adds specific capabilities (fuzzy search, multiple filter conditions).
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 alternatives like search_schemas or get_api_info. There are no exclusions, prerequisites, or indications of preferred use cases beyond the basic search purpose.
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 mentions fuzzy search and type filtering, but does not explicitly disclose the read-only nature, response format, or any side effects. For a search tool this is partially adequate.
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, front-loaded sentence with no filler. Every word adds value, clearly stating the action and key features.
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?
With no output schema and no annotations, the description does not explain what results look like or provide sufficient context for selecting this tool over siblings. The tool has four parameters, and the description is too minimal to be fully self-contained.
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 description coverage is 100%, so the baseline is 3. The description adds the 'fuzzy search' behavior, which is not explicitly stated in the query parameter description, providing additional meaning beyond the 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 'search schema definitions' (搜索 schema 定義) with specific features (fuzzy search and type filtering), distinguishing it from sibling tools like search_apis and get_schema.
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?
Usage is implied by the verb 'search' but there is no explicit guidance on when to use this tool versus siblings like get_schema or search_apis. No exclusions or alternatives are mentioned.
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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