Test Case Generator MCP Server

by prem2230

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Latest release: v1.0.0
Disambiguation5/5
Each tool targets a distinct operation: generation, reporting, manual creation, listing, status updates, project listing, export, and project deletion. Potential overlap between generate and add is clearly differentiated by automatic vs manual.
Naming Consistency4/5
Most tools follow verb_noun pattern (generate_test_cases, add_test_case, list_test_cases, update_status, delete_project). However, project_report deviates as a noun-based name, and delete_project could be misinterpreted as deleting the project rather than its test cases.
Tool Count5/5
With 8 tools covering generation, management, reporting, and export, the set is well-scoped for a test case generation server. Each tool serves a clear purpose without redundancy.
Completeness4/5
Core workflows are covered: generate/add, list, update status, report, export, and delete project. Missing individual test case deletion and editing details are minor gaps that don't break the primary use case.
Average 3.8/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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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?
No annotations are provided, so the description carries the full burden of disclosure. It says 'update' which implies mutation, but it does not state that the existing status will be overwritten, whether the operation is reversible, or if there are any side effects. It also omits the PENDING status from the list, which could mislead about allowed values. This is a significant transparency gap for a write operation.
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 includes the key status values. Every word earns its place; there is no fluff or repetition of schema information. Very concise and well-structured.
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 should clarify what the tool returns or how success is indicated. It does not mention whether the update is destructive to previous values, whether notes are replaced, or what the response format is. For a mutation tool, this is incomplete.
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 75% (id, project, notes have descriptions; status has enum only). The description adds context that the status is for after running a test, which helps interpret the status parameter. However, it lists only three of the four enum values (omitting PENDING), which is slightly misleading. Still, with high schema coverage, the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action: 'Update the status of a test case after running it', specifying the verb (update), resource (test case status), and context (after running). The enumerated statuses (PASS, FAIL, SKIPPED) further clarify the purpose. This distinguishes it from siblings like add_test_case (which creates) and list_test_cases (which lists).
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 phrase 'after running it' implies the intended use case (post-execution), but there is no explicit guidance on when to use this over alternatives, such as add_test_case for new test cases. The context is implied rather than stated, so it earns a 3.
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 must disclose behavioral traits. It only states the action without explaining side effects, validation, prerequisites (e.g., project must exist), or what happens on success/failure. For a mutating tool, this is insufficient.
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, straightforward sentence that communicates the core purpose without redundancy.
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 has 10 parameters, no output schema, and no annotations, a one-sentence description is insufficient. It fails to explain return behavior, error cases, or how this tool fits into the larger workflow alongside the listed siblings.
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?
The description adds no parameter-level information. With 60% schema coverage, the schema covers some parameters (tags, steps, title, feature, project, expected) but leaves others (type, priority, description, preconditions) less documented. The tool description does not compensate.
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 uses a specific verb 'add' and names the resource 'test case' with scope 'to a project'. The words 'Manually' and 'custom' distinguish it from sibling generate_test_cases, which implies automated generation.
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 phrase 'Manually add a single custom test case' gives clear context for when to use this tool (for manual, singular additions). It does not explicitly mention alternatives or exclusions, but the contrast with sibling generate_test_cases is implied.
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 must carry the full burden of behavioral disclosure. It discloses the destructive nature (deleting all test cases) but omits critical details: whether deletion is permanent, that the confirm parameter must be true, and whether the project itself is affected. This is insufficient for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no redundant words. It efficiently conveys the core operation while leaving schema details to the schema. This is an ideal size for this simple tool.
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 two-parameter tool with no output schema, the description covers the core action but misses important contextual nuance: it does not mention permanence, the confirm requirement (only in schema), or the boundary that only test cases are deleted, not the project. The description is minimal but adequate, though more behavioral context would improve completeness.
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 covers both parameters with clear descriptions (project name, confirm boolean). The description adds no additional semantic meaning beyond what the schema already provides. With 100% schema coverage, a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action: deleting all test cases for a project. It distinguishes from sibling tools like add_test_case and list_test_cases by specifying the deletion scope. The tool name 'delete_project' could be ambiguous, but the description resolves this by clarifying it deletes test cases, not the project itself.
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 when to use the tool: when you want to remove all test cases for a given project. It does not explicitly mention alternatives or exclusions, but the context is clear and it does not conflict with sibling tools. However, it lacks explicit guidance on prerequisites like project existence or confirmation behavior.
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 transparency burden. It discloses the output is a paste-ready Markdown document, suggesting a safe read operation, but omits details like authentication, error behavior, or whether the output is a file or raw text.
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. It efficiently conveys purpose and output format.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple export tool with only 2 parameters and no output schema, the description adequately explains the action and the result ('Markdown document'). It could mention edge cases like empty projects, but the purpose and output are sufficiently clear.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear descriptions for 'project' and 'feature'. The tool description adds little beyond confirming the export scope and the optional filter, so it meets the baseline without further enrichment.
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 uses a specific verb 'Export' with a clear resource 'all test cases for a project' and output 'Markdown document'. It distinguishes itself from siblings like list_test_cases and generate_test_cases by naming the export action and target format.
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 Markdown export is needed, but it does not explicitly contrast with siblings like project_report or state when not to use it. Context is clear but 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.
Behavior3/5
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. The word 'List' implies a read-only operation, and the filter options give some behavior, but it does not disclose return format, pagination, permission requirements, or any side effects. This is a moderate gap for a tool with 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, front-loaded with the core action and resource, followed by a concise list of filter options. Every word contributes value, with no redundancy or unnecessary detail.
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 list tool with a required project parameter and multiple optional filters, the description covers the main purpose and filter capabilities. However, without an output schema, it does not mention what fields are returned, whether pagination exists, or any usage constraints. It is adequate but leaves important context undeclared.
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 only 40%, so the description should compensate. It adds meaning by identifying feature, type, priority, and status as optional filters, and project is implied as the scope. However, it does not elaborate on allowed values or defaults, and the schema already provides enums for three parameters. It provides some additional context but not enough to fully compensate for the low schema coverage.
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 uses a specific verb 'List' with the resource 'test cases for a project' and explicitly mentions optional filters, making it clear what the tool does. It distinguishes itself from sibling tools like 'generate_test_cases', 'add_test_case', and 'list_projects' by focusing on listing existing test cases.
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 establishes clear context: this tool is for listing test cases under a project, with optional filters. While it does not explicitly contrast with alternatives or state when not to use it, the purpose itself implies its usage scenario and separates it from generation, updating, or project-level listing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool saves generated test cases to the project store (write behavior) and returns the full list, providing useful context beyond mere generation. It doesn't mention side effects like overwriting or project existence, but the disclosed behaviors are sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences, each serving a purpose: action, coverage scope, and side effect. No redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having no output schema, the description clearly explains the return value ('returns the full list') and the persistence behavior. The parameters are well-documented in the schema, and the tool's purpose is clear. It's sufficiently complete for an AI agent to select and invoke it correctly.
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 schema already provides 100% coverage with descriptions for all four parameters. The description doesn't add additional semantic meaning beyond the schema, other than implying that the 'description' parameter is central to the analysis. This is adequate per the baseline.
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 auto-generates comprehensive test cases from a feature/function description, with a specific verb ('auto-generate') and resource ('test cases'). It also lists coverage areas (happy path, edge cases, etc.) and distinguishes from sibling tools like add_test_case (manual) and list_test_cases (retrieval).
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 test cases need to be generated from a description but doesn't explicitly mention scenarios where this tool should be avoided or alternatives should be used. It lacks explicit 'when-not' guidance or references to sibling tools like add_test_case for manual entry.
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 the key filtering behavior (only projects with test cases are listed) and 'list' implies a read-only operation. However, it does not mention potential pagination, ordering, or whether 'all projects' includes archived or hidden projects. The description is minimal but acceptable given the tool's simplicity.
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. Every word contributes value, and there is no superfluous information. It is an example of concise, effective specification.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no parameters and no output schema, the description is complete enough: it states what the tool returns (a list of projects) and the filtering condition. It does not explain return value structure, but 'list' implies a collection, and the simple nature of the tool makes this sufficient. No major gaps are apparent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. The description adds meaning by clarifying the output scope—projects with test cases—which is not evident from the empty schema. Since there are no parameters to document, the description effectively covers the semantic context.
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 function: 'List all projects that have test cases stored.' It specifies the verb (list), the resource (projects), and a qualifying condition. This distinctly separates it from sibling tools like list_test_cases (which lists test cases, not projects) and generate_test_cases.
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 the usage context—when you need projects that have test cases—but does not explicitly state when to avoid this tool or mention alternatives. There is no guidance such as 'for projects without test cases, use X' or comparison to project_report. Thus, usage guidance is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations available, the description carries the full burden of behavioral disclosure. It clearly indicates a read-only operation ('Get') and details what the report contains, which provides meaningful context about expected behavior. While it does not disclose potential errors or performance characteristics, it sufficiently conveys the tool's primary behavior for a report-generation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that front-loads the action ('Get a summary report') and then provides concrete details. There is no redundant or extraneous information, making it highly concise and easily scannable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that the tool has a single parameter, no output schema, and no annotations, the description adequately explains what the tool does and what the return value includes (pass/fail counts, coverage by feature, pending items). This is sufficient for a user to understand the tool's purpose and expected results without additional context.
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 describes the only parameter 'project' as a string with description 'Project name'. The tool description adds minimal extra meaning by referencing 'in a project', which aligns with the schema. Since schema description coverage is 100%, the description does not need to compensate much, but it also does not enrich the parameter semantics 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 the tool's function: 'Get a summary report of all test cases in a project'. It specifies the resource (summary report) and scope (all test cases), and enumerates what the report includes (pass/fail counts, coverage by feature, pending items). This distinguishes it from sibling tools like list_test_cases, which would list individual cases rather than provide an aggregate summary.
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 through the phrase 'summary report' — guiding users to this tool when they need an overview rather than a detailed list. However, it does not explicitly state when to use this tool versus alternatives, nor does it provide any exclusions or when-not-to-use guidance. Thus, usage is implied but not clearly delineated.
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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