Exports generated test cases as Postman collections for API testing and automation within the Postman platform.
Parses Swagger/OpenAPI specifications to generate structured test cases, validate specs, compare versions, and create mock data.
Exports generated test cases in TestRail CSV format for importing into TestRail test management systems.
MCP server for API testing: generates test cases, validates specs, compares versions, and creates mock data from Swagger/OpenAPI specifications.
Feed it your API spec URL — get structured, categorized test cases (positive, negative, boundary, auth, security, idempotency, pagination) ready for manual testing or import into your TMS.
Writing test cases manually is slow. A single endpoint with 10+ parameters can require 30-50 test cases across positive, negative, boundary, and security scenarios. Multiply that by dozens of endpoints — and you're spending days on routine work instead of actual testing.
Negative and boundary cases get skipped. Under deadline pressure, QA focuses on happy paths. Edge cases, invalid inputs, and security checks are the first to be cut — and the first to cause production incidents.
Specs change faster than test docs, and internal APIs get left behind. New endpoints, renamed fields, changed constraints — keeping test cases in sync is constant overhead. Corporate APIs behind VPN or custom auth make it even harder, since most tooling doesn't support authenticated spec access.
Getting test cases into your TMS takes extra steps. Even if you write great test cases, formatting them for TestRail, Allure, or Postman import is tedious work that adds no value.
This tool automates the baseline: point it at any Swagger/OpenAPI spec (public, internal, localhost), get categorized test cases in seconds, and save ready-to-import files for your TMS or test runner. You focus on business logic and exploratory testing — the tool handles the rest.
Related MCP server: mcp-playwright-test
Parses Swagger 2.0 and OpenAPI 3.x — JSON and YAML
Smart test case generation — analyzes parameters, schemas, constraints, response codes, and security to produce relevant test cases
8 test categories — positive, negative, boundary, authorization, business logic, security injection, idempotency, pagination & sorting
9 export formats — Markdown, JSON, CSV, Allure CSV, Gherkin, Postman, k6, pytest, TestRail CSV (two templates: Steps and Text)
Spec validation — quality scoring with 11 rules and actionable suggestions
Spec comparison — diff two versions with breaking change detection
Mock data generation — realistic test data from schemas with locale support
Coverage analysis — identify gaps in test coverage with prioritized recommendations
Auto-save to file — exports are saved to the working directory automatically, no manual copy-paste
Configurable generation — filter by category, priority, locale, custom preconditions
Batch generation — filter by tag, HTTP method, path prefix, deprecated status
Works with any MCP client — Claude Desktop, Cursor, VS Code + Cline, etc.
Category | What it covers |
✅ Positive | Happy path with required fields, happy path with all fields |
❌ Negative | Missing required fields, invalid types, empty strings, nulls, invalid enums, empty body |
📏 Boundary | Min/max values for numbers, minLength/maxLength for strings |
🔐 Auth | No token, invalid token, insufficient permissions |
💼 Business Logic | 404 for non-existent resources, 409 conflicts, 429 rate limiting, concurrency |
🛡️ Security | XSS, SQL injection, path traversal, command injection, CRLF injection |
🔄 Idempotency | Repeated PUT/DELETE, POST with Idempotency-Key |
📄 Pagination | First/last page, negative offset, zero limit, invalid sort fields |
npm install -g swagger-testcase-mcpOr run directly with npx:
npx swagger-testcase-mcpAdd to claude_desktop_config.json:
{
"mcpServers": {
"swagger-testcase-mcp": {
"command": "npx",
"args": ["-y", "swagger-testcase-mcp"]
}
}
}Add to .cursor/mcp.json:
{
"mcpServers": {
"swagger-testcase-mcp": {
"command": "npx",
"args": ["-y", "swagger-testcase-mcp"]
}
}
}Add to .vscode/mcp.json:
{
"servers": {
"swagger-testcase-mcp": {
"command": "npx",
"args": ["-y", "swagger-testcase-mcp"]
}
}
}Add to Cline MCP settings:
{
"swagger-testcase-mcp": {
"command": "npx",
"args": ["-y", "swagger-testcase-mcp"]
}
}Go to Settings → Tools → AI Assistant → MCP Servers → Add, select stdio and fill in:
Command: npx
Arguments: -y swagger-testcase-mcp
Or add manually to the MCP config file:
{
"servers": {
"swagger-testcase-mcp": {
"command": "npx",
"args": ["-y", "swagger-testcase-mcp"]
}
}
}Add to ~/.windsurf/mcp.json:
{
"mcpServers": {
"swagger-testcase-mcp": {
"command": "npx",
"args": ["-y", "swagger-testcase-mcp"]
}
}
}1. "Load the spec from https://petstore3.swagger.io/api/v3/openapi.json"
2. "Generate test cases for POST /pet"
3. "Export as Postman collection"Here is what the generated output looks like (markdown export):
## Positive Tests (1)
### TC-001: POST /pet — happy path with required fields only
- **Priority:** high
- **Preconditions:** Valid authentication credentials are available
- **Steps:**
1. Send POST request to /pet
2. Include only required parameters/fields with valid values
3. Verify response status and body
- **Input:** `{"name":"Buddy","photoUrls":["https://example.com/photo.jpg"]}`
- **Expected Result:** Returns success response with expected data
- **Expected Status:** 200
## Negative Tests (2)
### TC-002: POST /pet — missing required field "name"
- **Priority:** high
- **Preconditions:** Valid authentication credentials are available
- **Steps:**
1. Send POST request to /pet
2. Omit required field "name"
3. Verify error response
- **Input:** `{"_omit":"name"}`
- **Expected Result:** Returns validation error indicating "name" is required
- **Expected Status:** 422
### TC-003: POST /pet — invalid type for "name"
- **Priority:** medium
- **Preconditions:** Valid authentication credentials are available
- **Steps:**
1. Send POST request to /pet
2. Set "name" to invalid type value: 12345
3. Verify error response
- **Input:** `{"name":12345}`
- **Expected Result:** Returns validation error for invalid type of "name"
- **Expected Status:** 422The generator produces test cases across 8 categories (positive, negative, boundary, auth, business logic, security, idempotency, pagination) -- the example above shows just a small subset. Use generate_test_cases_batch to cover an entire API at once.
For detailed examples of every tool and workflow, see EXAMPLES.md.
Tool | Description |
| Load and parse a spec from URL, internal URL, or local file (with optional auth) |
| Get detailed info about a specific endpoint |
| Generate test cases for one endpoint (with optional config) |
| Generate test cases for multiple endpoints (filter by tag/method/path prefix) |
| Export test cases and save to file (markdown, json, csv, allure_csv, gherkin, postman, k6, pytest, testrail_csv, testrail_csv_text) |
| Compare two spec versions, detect breaking changes |
| Validate spec quality (score 0-100, 12 rules) |
| Generate realistic mock data from schemas |
| Analyze test coverage and suggest improvements |
| Clear cached specs and/or test cases |
All generation tools accept an optional config parameter:
{
"config": {
"categories": ["positive", "negative", "security"],
"priorityFilter": ["high"],
"maxFieldsForNegative": 15,
"locale": "ru",
"customPreconditions": ["Database seeded with test data"],
"skipDeprecated": true
}
}Parameter | Description |
| Which test categories to generate: |
| Only generate cases with these priorities: |
| Max number of fields for invalid-type negative cases. Default: 10 |
| Language for test case text: |
| Additional preconditions added to all generated cases |
| Skip deprecated endpoints in batch generation. Default: |
All exports are automatically saved to the working directory. The filename is generated from the endpoint and format:
test-cases_POST__pet_postman.json
test-cases_GET__users_testrail_csv.csv
test-cases_POST__orders_k6.jsYou can also specify a custom path:
"Export as postman to /path/to/my-collection.json"Format | File extension |
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The tool generates test cases based on what is described in the OpenAPI spec — parameters, schemas, constraints, response codes, and security definitions. It does not cover:
Business logic — rules like "discount cannot exceed 50% for non-admin users" or "order requires at least one item in stock" are not part of the spec and won't produce test cases automatically
Cross-endpoint dependencies — the tool works with each endpoint in isolation. It won't generate chains like "create user → create order → check status"
Custom validation rules — constraints beyond what OpenAPI supports (e.g., conditional field requirements, cross-field validation) are not detected
How to compensate: since the tool runs inside an MCP client (Claude, Cursor, etc.), you can describe your business rules in the chat and ask the LLM to extend or adjust the generated test cases. The tool provides the baseline, the LLM adds the context.
Tip: The better your OpenAPI spec (constraints, enums, examples, descriptions), the more relevant the generated test cases will be. Use
validate_specto check your spec quality and get suggestions for improvement.
Works with any Swagger/OpenAPI spec — public, internal corporate network, localhost, or local files.
"Load the spec from https://petstore3.swagger.io/api/v3/openapi.json"
"Load the spec from /path/to/swagger.json"
"Load the spec from ./api-spec.yaml"
"Load the spec from http://localhost:8080/v3/api-docs"Access specs behind authentication — VPN, SSO, or internal networks:
"Load the spec from https://internal-api.company.com/docs/swagger.json with auth_header Bearer eyJhbGci..."{
"source": "https://internal-api.company.com/docs/swagger.json",
"auth_header": "Bearer eyJhbGciOiJIUzI1NiIs..."
}{
"source": "https://internal-api.company.com/docs/swagger.json",
"auth_header": "Basic dXNlcjpwYXNz"
}{
"source": "https://internal-api.company.com/docs/swagger.json",
"headers": { "X-API-Key": "abc123" }
}Tip: If your spec is behind a corporate VPN, make sure the MCP server process has network access to the internal URL. The server runs locally, so VPN or proxy settings on your machine apply automatically.
Generate test cases and export as allure_csv
Import via the Allure TestOps migration tool (configure fieldMapping to match the columns below)
Columns: Name, Description, Precondition, Steps, Expected Result, Layer, Tags
Or combine with the Allure TestOps MCP server to create test cases directly via API
Note: CSV import through the Allure TestOps UI was removed in version 25.3.4. Use the migration tool or API integration instead.
"Generate test cases for POST /pet"
"Export as allure_csv"Two export formats are available:
testrail_csv — Steps template (one row per step). Expected result is attached to the last step of each test case; intermediate steps have empty expected results.
testrail_csv_text — Text template (all steps in one field). Simpler, works with any TestRail configuration.
Steps template (recommended):
Export as testrail_csv
In TestRail, go to Test Cases → Import from CSV
Select the "Test Case (Steps)" template
Map columns: Title, Section, Type, Priority, Preconditions, Step, Expected Result
"Generate test cases for all endpoints tagged 'users'"
"Export as testrail_csv"Text template:
"Export as testrail_csv_text"Most TMS platforms support CSV import. Use the csv format and map columns to your TMS fields during import.
Qase — map title, preconditions, steps, expected_result, priority
Zephyr Scale — map Name, Precondition, Test Script (Plain Text), Priority, Folder
TestLink — use the standard CSV import wizard
"Generate test cases for GET /users"
"Export as csv"Generate test cases and export as a Postman Collection v2.1 that can be imported directly into Postman or run via Newman in CI:
"Load the spec from https://internal-api.company.com/docs/swagger.json with auth_header Bearer eyJ..."
"Generate test cases for POST /users"
"Export as postman"The exported collection includes:
Organized folders by test category (positive, negative, security, etc.)
Pre-configured HTTP method, URL, headers, and request body for each test case
Auto-generated test scripts with expected status code assertions
Open Postman → Import → drag the exported JSON file
The collection appears with folders for each endpoint
Set up an environment with baseUrl variable pointing to your API
newman run exported-collection.json \
--environment env.json \
--reporters cli,junitgit clone https://github.com/eyaushev/swagger-testcase-mcp.git
cd swagger-testcase-mcp
npm install
npm run build
npm test
npm startMIT
Analyze a specific endpoint from a loaded Swagger spec. Returns detailed info about parameters, request body, responses, and security.
| Name | Required | Description | Default |
|---|---|---|---|
| source | Yes | Swagger/OpenAPI spec source: URL (https://...) or local file path (/path/to/spec.json, ./spec.yaml) | |
| path | Yes | Endpoint path, e.g. /api/orders | |
| method | Yes | HTTP method | |
| auth_header | No | Authorization header value, e.g. "Bearer eyJ..." or "Basic dXNlcjpwYXNz" | |
| headers | No | Additional HTTP headers as key-value pairs, e.g. {"X-API-Key": "abc123"} |
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 mentions that the tool returns detailed info, but doesn't specify whether this is a read-only operation, if it requires authentication, potential rate limits, or error handling. For a tool with 5 parameters and no annotations, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose and lists return values. Every word earns its place, though it could be slightly more structured (e.g., separating purpose from output). No wasted words or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (5 parameters, no output schema, no annotations), the description is incomplete. It doesn't address authentication needs, error cases, or how this tool fits with siblings like 'fetch_swagger'. For a tool that likely interacts with external specs and returns detailed data, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, meaning all parameters are documented in the schema. The description doesn't add any parameter-specific details beyond what's in the schema (e.g., it doesn't explain how 'source' relates to 'fetch_swagger' or format requirements). Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: analyzing a specific endpoint from a loaded Swagger spec. It specifies the verb 'analyze' and the resource 'endpoint', and mentions what information is returned (parameters, request body, responses, security). However, it doesn't explicitly differentiate this tool from its siblings like 'validate_spec' or 'compare_specs', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 prerequisites (e.g., needing a loaded spec first), nor does it compare with siblings like 'validate_spec' for validation or 'generate_test_cases' for test generation. The agent must infer usage from the description alone, which is insufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Clear cached specs and/or generated test cases. Useful when the spec has been updated and needs to be reloaded.
| Name | Required | Description | Default |
|---|---|---|---|
| target | Yes | What to clear: specs, test_cases, or all |
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 'clears' cached data, implying a destructive operation, but doesn't specify whether this is reversible, requires permissions, has side effects, or provides confirmation. For a mutation tool with zero annotation coverage, this leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences with zero waste. The first sentence states the action and target, while the second provides usage context—both sentences earn their place efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (a destructive operation with one parameter) and no annotations or output schema, the description is adequate but incomplete. It explains the purpose and usage context but lacks details on behavioral implications (e.g., effects, safety) that would be needed for full understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the parameter 'target' fully documented in the schema (including enum values and description). The description mentions 'specs and/or generated test cases' which aligns with the schema but doesn't add meaningful semantics beyond what's already structured.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('clear') and resource ('cached specs and/or generated test cases'), making the purpose unambiguous. However, it doesn't explicitly distinguish this tool from its siblings (like 'analyze_endpoint' or 'validate_spec'), which would require a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('when the spec has been updated and needs to be reloaded'), which is helpful guidance. It doesn't specify when NOT to use it or name alternatives among siblings, preventing a score of 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compare two versions of an OpenAPI spec. Detects added/removed/modified endpoints, schema changes, and breaking changes.
| Name | Required | Description | Default |
|---|---|---|---|
| old_source | Yes | Old spec source: URL or file path | |
| new_source | Yes | New spec source: URL or file path | |
| auth_header | No | Authorization header value, e.g. "Bearer eyJ..." or "Basic dXNlcjpwYXNz" | |
| headers | No | Additional HTTP headers as key-value pairs, e.g. {"X-API-Key": "abc123"} |
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 mentions the tool 'detects' changes but doesn't specify output format, whether it's read-only or has side effects, error handling, or performance considerations. For a tool with 4 parameters and no annotations, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose and details key functionalities. Every word earns its place, with no redundancy or fluff, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (comparing OpenAPI specs with 4 parameters, no output schema, and no annotations), the description is incomplete. It lacks details on output format, error cases, authentication needs implied by 'auth_header', and how breaking changes are reported. This leaves the agent under-informed for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 parameter-specific information beyond implying the tool compares 'two versions', which aligns with 'old_source' and 'new_source'. It doesn't explain parameter interactions or provide examples beyond what the schema offers.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 ('Compare') and resources ('two versions of an OpenAPI spec'), and distinguishes it from siblings by focusing on comparison rather than analysis, validation, or generation. It explicitly mentions what gets compared: 'added/removed/modified endpoints, schema changes, and breaking changes'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 'analyze_endpoint' or 'validate_spec'. It doesn't mention prerequisites, such as needing accessible spec sources, or exclusions, like not being suitable for single-spec validation. Usage context is implied but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Export previously generated test cases in various formats: markdown, json, csv, allure_csv, gherkin, postman, k6, pytest, testrail_csv (Steps template), testrail_csv_text (Text template).
| Name | Required | Description | Default |
|---|---|---|---|
| endpoint | Yes | Endpoint key like "POST /api/orders", or "_last_batch" for the last batch generation | |
| format | Yes | Export format. testrail_csv uses Steps template (one row per step), testrail_csv_text uses Text template (all steps in one field) | |
| output_path | No | Custom file path to save the export. If omitted, saves automatically to the working directory with a generated filename. |
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. It mentions export formats and file saving behavior, but lacks critical details like whether this operation requires specific permissions, if it's idempotent, what happens on failure, or if there are rate limits. For a tool that presumably writes files, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured as a single sentence that front-loads the core purpose and follows with a comprehensive list of supported formats. Every element serves a clear purpose with zero wasted words, making it easy to parse while being information-dense.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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, file output operation) and lack of both annotations and output schema, the description is incomplete. It covers what formats are available but doesn't address error conditions, output structure, or operational constraints that would help an agent use it correctly in various scenarios.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 three parameters thoroughly. The description adds minimal value beyond the schema by listing format options, but doesn't provide additional context about parameter interactions, default behaviors, or practical examples. 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Export') and resource ('previously generated test cases'), and distinguishes this tool from siblings like 'generate_test_cases' or 'generate_test_cases_batch' by focusing on exporting existing content rather than creating new content. The verb+resource combination is precise and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by specifying 'previously generated test cases,' suggesting this tool should be used after test generation. However, it doesn't explicitly state when to use this versus alternatives like 'analyze_endpoint' or 'validate_spec,' nor does it provide exclusion criteria or prerequisites for successful export operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch and parse an OpenAPI/Swagger specification. Supports:
Public URLs: https://api.example.com/swagger.json
Internal URLs (accessible from user's machine/VPN): https://internal-api.company.com/docs/swagger.json
Local files: /path/to/swagger.json, ./api-spec.yaml
Auth-protected specs: pass auth_header for Bearer/Basic/API-Key auth
| Name | Required | Description | Default |
|---|---|---|---|
| source | Yes | Swagger/OpenAPI spec source: URL (https://...) or local file path (/path/to/spec.json, ./spec.yaml) | |
| auth_header | No | Authorization header value, e.g. "Bearer eyJ..." or "Basic dXNlcjpwYXNz" | |
| headers | No | Additional HTTP headers as key-value pairs, e.g. {"X-API-Key": "abc123"} |
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 effectively describes the tool's behavior regarding source types and authentication, but doesn't mention error handling, rate limits, parsing behavior, or what the parsed output looks like (though there's no output schema).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly structured and concise - a clear opening sentence followed by a bulleted list of supported source types, with every sentence earning its place. No wasted words or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 3 parameters, no annotations, and no output schema, the description provides good coverage of input sources and authentication but lacks information about parsing behavior, error conditions, and what the agent should expect as output from this fetch-and-parse operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 three parameters thoroughly. The description adds some context about auth_header usage ('Bearer/Basic/API-Key auth') but doesn't provide significant additional parameter semantics beyond what's in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 ('fetch and parse') and resource ('OpenAPI/Swagger specification'), distinguishing it from siblings like 'validate_spec' or 'analyze_endpoint' which operate on already-loaded specifications rather than fetching them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 by listing supported source types (public URLs, internal URLs, local files, auth-protected specs), but it doesn't explicitly state when NOT to use it or mention alternatives like 'validate_spec' for already-loaded specs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate realistic mock/sample data from an endpoint's request or response schema. Useful for populating Postman, tests, or documentation.
| Name | Required | Description | Default |
|---|---|---|---|
| source | Yes | Swagger/OpenAPI spec source: URL (https://...) or local file path (/path/to/spec.json, ./spec.yaml) | |
| path | Yes | Endpoint path, e.g. /api/orders | |
| method | Yes | HTTP method | |
| target | Yes | Generate data for "request" body or "response" body | |
| response_code | No | Response status code (default: first 2xx). Only used when target="response" | |
| count | No | Number of mock objects to generate (default: 1) | |
| format | No | Output format (default: json) | |
| locale | No | Locale for generated names/addresses (default: en) | |
| use_examples | No | Prefer example values from spec (default: true) | |
| auth_header | No | Authorization header value, e.g. "Bearer eyJ..." or "Basic dXNlcjpwYXNz" | |
| headers | No | Additional HTTP headers as key-value pairs, e.g. {"X-API-Key": "abc123"} |
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. While it mentions the tool is 'useful for populating Postman, tests, or documentation,' it lacks critical behavioral details: it doesn't specify if this makes actual API calls, whether it requires network access, what the output looks like (e.g., JSON structure), or any performance/rate limit considerations. The description is too high-level for a tool with 11 parameters and no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise and front-loaded. The first sentence clearly states the core purpose, and the second sentence adds practical context without redundancy. Every sentence earns its place, and there's no wasted verbiage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (11 parameters, no output schema, and no annotations), the description is incomplete. It doesn't explain what the tool returns (e.g., sample data format, error handling), behavioral aspects like whether it performs network operations, or prerequisites like needing a valid OpenAPI spec. For a data generation tool with many inputs and unknown outputs, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, meaning all parameters are documented in the schema. The description adds no parameter-specific information beyond what's in the schema. It mentions generating data 'from an endpoint's request or response schema,' which aligns with the 'source', 'path', 'method', and 'target' parameters but doesn't provide additional semantics. Baseline score of 3 is appropriate since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate realistic mock/sample data from an endpoint's request or response schema.' It specifies the verb ('generate'), resource ('mock/sample data'), and source ('from an endpoint's request or response schema'). However, it doesn't explicitly differentiate from sibling tools like 'generate_test_cases' or 'export_test_cases', which may have overlapping purposes in testing contexts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides implied usage context: 'Useful for populating Postman, tests, or documentation.' This gives some guidance on when to use the tool, but it doesn't explicitly state when not to use it or mention alternatives. For example, it doesn't clarify if this should be used instead of 'generate_test_cases' for mock data generation versus test case creation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate QA test cases for a specific API endpoint. Produces positive, negative, boundary, auth, security, idempotency, pagination, and business logic test cases based on the OpenAPI spec.
| Name | Required | Description | Default |
|---|---|---|---|
| source | Yes | Swagger/OpenAPI spec source: URL (https://...) or local file path (/path/to/spec.json, ./spec.yaml) | |
| path | Yes | Endpoint path, e.g. /api/orders | |
| method | Yes | HTTP method | |
| auth_header | No | Authorization header value, e.g. "Bearer eyJ..." or "Basic dXNlcjpwYXNz" | |
| headers | No | Additional HTTP headers as key-value pairs, e.g. {"X-API-Key": "abc123"} | |
| config | No | Generation configuration options |
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 what the tool produces (test cases) but lacks details on execution behavior such as runtime, error handling, or output format. It mentions the tool is based on OpenAPI spec, which is useful context, but does not cover other behavioral traits like performance or limitations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose and key details (types of test cases and data source). Every word earns its place with no redundancy or unnecessary elaboration, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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, nested objects) and lack of annotations or output schema, the description is adequate but has gaps. It covers the purpose and output types but does not address behavioral aspects like how test cases are formatted or delivered. For a tool with rich parameter schema but no output schema, more context on results would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 6 parameters thoroughly. The description adds no parameter-specific information beyond implying the 'source' parameter relates to OpenAPI spec. Baseline 3 is appropriate as the schema does the heavy lifting, with minimal value added by the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('generate') and resource ('QA test cases for a specific API endpoint'), specifying the types of test cases produced (positive, negative, boundary, etc.) and the data source (OpenAPI spec). It distinguishes from sibling tools like 'analyze_endpoint' or 'suggest_missing_tests' by focusing on comprehensive test case generation rather than analysis or suggestion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by mentioning the OpenAPI spec source and endpoint specificity, but does not explicitly state when to use this tool versus alternatives like 'generate_test_cases_batch' or 'suggest_missing_tests'. It provides some guidance through the config parameter descriptions (e.g., 'For functional testing use...'), but this is in the schema, not the main description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate QA test cases for multiple endpoints at once. Optionally filter by tag, methods, or path prefix.
| Name | Required | Description | Default |
|---|---|---|---|
| source | Yes | Swagger/OpenAPI spec source: URL (https://...) or local file path (/path/to/spec.json, ./spec.yaml) | |
| tag | No | Filter endpoints by tag (optional) | |
| methods | No | Filter by HTTP methods, e.g. ['POST', 'PUT'] (optional) | |
| path_prefix | No | Filter by path prefix, e.g. '/api/v2' (optional) | |
| auth_header | No | Authorization header value, e.g. "Bearer eyJ..." or "Basic dXNlcjpwYXNz" | |
| headers | No | Additional HTTP headers as key-value pairs, e.g. {"X-API-Key": "abc123"} | |
| config | No | Generation configuration options |
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 tool generates test cases but doesn't describe what that entails (e.g., output format, whether it's a read-only operation, potential side effects like caching, or performance considerations). For a tool with 7 parameters and no annotations, this is a significant gap in transparency about how the tool behaves beyond its basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—just two short sentences—with zero wasted words. It front-loads the core purpose and efficiently lists optional features. Every sentence earns its place by conveying essential information without redundancy or fluff, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (7 parameters, nested objects, no output schema, and no annotations), the description is incomplete. It doesn't explain what the tool outputs (e.g., test case format, file type, or structure), behavioral aspects like rate limits or permissions, or how it integrates with sibling tools. For a batch generation tool with significant configuration options, this leaves too many gaps for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 minimal value by hinting at filtering capabilities ('filter by tag, methods, or path prefix'), which aligns with some parameters but doesn't provide additional syntax, format details, or context beyond what's in the schema. This meets the baseline for high schema coverage but doesn't enhance understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate QA test cases for multiple endpoints at once.' It specifies the verb ('generate'), resource ('QA test cases'), and scope ('multiple endpoints at once'), which is specific and actionable. However, it doesn't explicitly differentiate from sibling tools like 'generate_test_cases' (singular vs. batch) or 'suggest_missing_tests', leaving some ambiguity about when to choose this over alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides implied usage context by mentioning filtering options ('Optionally filter by tag, methods, or path prefix'), which suggests this tool is for bulk generation with optional scoping. However, it lacks explicit guidance on when to use this tool versus alternatives like 'generate_test_cases' (singular) or 'suggest_missing_tests', and doesn't mention prerequisites or exclusions (e.g., when not to use it). The guidance is present but incomplete.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Analyze test coverage for generated test cases. Shows which response codes, parameters, and boundary conditions are covered or missing. Provides prioritized suggestions for additional tests.
| Name | Required | Description | Default |
|---|---|---|---|
| source | Yes | Swagger/OpenAPI spec source: URL (https://...) or local file path (/path/to/spec.json, ./spec.yaml) | |
| endpoint | No | Specific endpoint like "POST /api/orders". Omit for full spec analysis. | |
| auth_header | No | Authorization header value, e.g. "Bearer eyJ..." or "Basic dXNlcjpwYXNz" | |
| headers | No | Additional HTTP headers as key-value pairs, e.g. {"X-API-Key": "abc123"} |
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 describes what the tool does ('analyze', 'shows', 'provides') but lacks critical behavioral details such as whether it performs read-only analysis, requires authentication, has rate limits, or what the output format looks like. The description is functional but insufficient for a tool with 4 parameters and no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and well-structured in two sentences. The first sentence states the core purpose, the second elaborates on outputs and value. Every word earns its place with zero redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 parameters, no output schema, no annotations), the description is incomplete. It explains what the tool does but fails to address behavioral aspects like authentication needs, output format, or error handling. Without annotations or output schema, the agent lacks sufficient context to use this tool effectively beyond basic invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 does not add any parameter-specific semantics beyond what the schema provides (e.g., it doesn't explain how 'source' relates to test coverage analysis or clarify parameter interactions). Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 ('analyze', 'shows', 'provides') and resources ('test coverage for generated test cases', 'response codes, parameters, and boundary conditions'). It distinguishes from siblings like 'generate_test_cases' by focusing on analysis of existing coverage rather than generation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for analyzing test coverage and identifying gaps, but does not explicitly state when to use this tool versus alternatives like 'analyze_endpoint' or 'validate_spec'. No exclusions or prerequisites are mentioned, leaving the agent to infer context from the tool name and description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Validate an OpenAPI spec for quality and completeness. Checks for missing descriptions, orphaned schemas, naming inconsistencies, missing error responses, and more. Returns a quality score (0-100).
| Name | Required | Description | Default |
|---|---|---|---|
| source | Yes | Swagger/OpenAPI spec source: URL (https://...) or local file path (/path/to/spec.json, ./spec.yaml) | |
| auth_header | No | Authorization header value, e.g. "Bearer eyJ..." or "Basic dXNlcjpwYXNz" | |
| headers | No | Additional HTTP headers as key-value pairs, e.g. {"X-API-Key": "abc123"} |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses key behaviors: it performs validation checks (e.g., for missing descriptions), returns a quality score (0-100), and implies it may fetch specs from URLs or local files. However, it does not mention authentication needs (though hinted by 'auth_header' parameter), rate limits, or error handling, leaving gaps for a tool with network/file operations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by specific checks and output, all in two efficient sentences with zero waste. Every sentence earns its place by adding value (e.g., listing validation aspects clarifies scope).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is moderately complete. It covers the tool's purpose and output (quality score), but lacks details on behavioral traits (e.g., network/file access implications) and does not fully compensate for the absence of structured fields. For a tool with 3 parameters and potential complexity (network calls, validation logic), it should do more to guide usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 (source, auth_header, headers). The description adds no additional parameter semantics beyond what's in the schema, such as explaining how 'source' interacts with validation or when 'auth_header' is required. 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.
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 ('validate', 'checks') and resources ('OpenAPI spec'), listing concrete validation aspects (missing descriptions, orphaned schemas, etc.) and the output (quality score). It distinguishes from siblings like 'analyze_endpoint' or 'compare_specs' by focusing on comprehensive spec validation rather than endpoint analysis or comparison.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for validating OpenAPI specs for quality, but does not explicitly state when to use this tool versus alternatives like 'analyze_endpoint' (for specific endpoints) or 'compare_specs' (for comparing multiple specs). It provides some context (e.g., checks for missing descriptions) but lacks explicit guidance on prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0analyze_endpointclear_cachecompare_specsexport_test_casesfetch_swaggergenerate_mock_datagenerate_test_casesgenerate_test_cases_batchsuggest_missing_testsvalidate_specEach tool has a clearly distinct purpose with no overlap: analyze_endpoint, compare_specs, export_test_cases, fetch_swagger, generate_mock_data, generate_test_cases, generate_test_cases_batch, suggest_missing_tests, and validate_spec all target specific, non-overlapping tasks in the API testing workflow. Even clear_cache is distinct as a utility function. An agent can easily differentiate between them based on their descriptions.
Tool names follow a consistent snake_case pattern with clear verb_noun or verb_adjective_noun structures throughout, such as analyze_endpoint, clear_cache, compare_specs, export_test_cases, fetch_swagger, generate_mock_data, generate_test_cases, generate_test_cases_batch, suggest_missing_tests, and validate_spec. This uniformity makes the set predictable and easy to navigate.
With 10 tools, the server is well-scoped for its purpose of API testing and validation. Each tool serves a specific, necessary function in the workflow—from fetching and validating specs to generating and analyzing test cases—without redundancy. This count is typical for a domain-focused server and supports comprehensive operations without being overwhelming.
The tool set provides complete coverage for the API testing domain: it handles spec loading (fetch_swagger), validation (validate_spec), analysis (analyze_endpoint, compare_specs), test generation (generate_test_cases, generate_test_cases_batch), mock data creation (generate_mock_data), coverage assessment (suggest_missing_tests), export (export_test_cases), and utility (clear_cache). There are no obvious gaps; agents can perform end-to-end workflows without dead ends.
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MCP server for AI access to Swagger by SmartBear.
AI-native mock API server with MCP. Create REST/SOAP mocks from Claude, Cursor, or Windsurf.
MCP server for AI access to SmartBear tools, including BugSnag, Reflect, Swagger, PactFlow, QTM4J.
An MCP server that provides access to Testiny projects, test cases and test runs
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