The MCP QA Lab server offers a comprehensive set of tools for evidence-based quality testing of MCP servers:
Target Management: Register stdio or Streamable HTTP targets securely by referencing environment variable names (never values) and list them without exposing secrets. Local targets are restricted to safe working directories.
Live Inspection: Connect to a target and capture its complete, paginated model-facing contract (tools, prompts, resources, and resource templates).
Static Analysis: Deterministically analyze schemas, descriptions, annotations, naming, and safety hints to identify usability risks without invoking tools.
Context-Cost Measurement: Measure serialized size and approximate token cost of model-facing metadata, and detect duplicate descriptions.
Scenario Generation: Use host-model sampling (with fallback) to propose task-oriented, multi-tool test journeys based on a user-defined objective.
Controlled Tool Execution: Invoke a target tool with reviewed JSON arguments, requiring explicit approval for tools not annotated as read-only or with side effects.
QA Reports: Build redacted Markdown reports from live inspection and deterministic checks, with credentials, tokens, and headers automatically removed.
Safety Defaults: Deny remote targets unless explicitly allowed, restrict local working directories, never persist environment variable values, and redact sensitive information.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP QA Lab Register a new MCP server for testing"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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Evidence-driven quality testing for Model Context Protocol servers. MCP QA Lab connects as a real client, captures the complete model-facing contract, finds agent-usability risks, measures context cost, and runs only explicitly approved target calls.
A server can have perfect handler-level tests and still be difficult for an agent to use. Tool names may be ambiguous, input fields may be undocumented, pagination may be incomplete, and safety intent may be missing. MCP QA Lab tests that public protocol surface separately from implementation code.
flowchart LR
A[Register target] --> B[Live protocol inspection]
B --> C[Static contract checks]
B --> D[Context-cost measurement]
C --> E[Scenario generation]
E --> F[Approved calls only]
D --> G[Evidence report]
F --> GRelated MCP server: MCProbe
Complete paginated discovery of tools, prompts, resources, and resource templates
Deterministic checks for schema quality, naming, descriptions, and safety annotations
Model-facing contract size and approximate context-cost measurement
Host-model sampling for task-oriented scenario proposals
Explicit approval boundary for target tool calls with possible side effects
Secret-free target registration using environment variable names, never values
Redacted Markdown reports with reproducible evidence
Stdio and Streamable HTTP target support
Install directly from the public repository:
uv tool install "git+https://github.com/efficjump/mcp-qa-lab.git"
mcp-qa-lab --transport stdioFor source development:
git clone https://github.com/efficjump/mcp-qa-lab.git
cd mcp-qa-lab
uv sync --all-extras --locked
uv run mcp-qa-lab --transport stdio{
"mcpServers": {
"mcp-qa-lab": {
"command": "mcp-qa-lab",
"args": ["--transport", "stdio"]
}
}
}Client configuration formats vary; the command and arguments above do not require a local repository path after tool installation.
Tool | Purpose |
| Persist a secret-free stdio or Streamable HTTP target definition |
| List registered targets |
| Capture the live, paginated MCP contract |
| Find deterministic contract and usability issues |
| Measure serialized contract size and approximate token cost |
| Ask the host model for evidence-oriented test journeys |
| Invoke one reviewed target tool with safety approval |
| Write a redacted Markdown QA report |
Remote targets are denied unless MCP_QA_ALLOW_REMOTE=1.
Local target working directories must stay inside MCP_QA_ALLOWED_ROOTS when configured.
Registrations reference inherited environment variable names; values are never persisted.
Inspection and static checks never invoke target tools.
Tools without readOnlyHint: true require explicit side-effect approval.
Reports redact credentials, authorization headers, tokens, and URL user information.
The process boundary is not an operating-system sandbox. Run untrusted targets in an isolated environment.
mcp-qa-lab --transport streamable-http --host 127.0.0.1 --port 8765The default endpoint is http://127.0.0.1:8765/mcp. Keep the listener on loopback unless a separate
authentication and network boundary is in place.
uv sync --all-extras --locked
uv run ruff format --check .
uv run ruff check .
uv run mypy src
uv run pytest --cov --cov-report=term-missing
uv buildThe coverage gate is 85%. See architecture, security policy, and contribution guide.
Create a redacted Markdown report from a new live inspection and deterministic checks.
| Name | Required | Description | Default |
|---|---|---|---|
| target_id | Yes | Identifier returned by register_target. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate non-read-only and non-destructive behavior. The description adds that the output is 'redacted' and that the report is based on 'a new live inspection', hinting at side effects (possibly triggering a new inspection). However, it does not clarify whether the tool itself initiates the inspection or consumes prior results, leaving some transparency 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, front-loaded sentence with no extraneous words, covering the core action and inputs efficiently. Every word contributes meaning.
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 simple one-parameter tool with an output schema, the description covers the essential purpose and inputs. However, it does not specify whether the inspection is triggered automatically or must be run previously, leaving a minor ambiguity about the execution flow.
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 only parameter, target_id, is fully described in the schema ('Identifier returned by register_target'), and the tool description adds no additional parameter semantics. Since schema coverage is 100%, 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Create' with a clear resource ('redacted Markdown report') and identifies its data sources ('new live inspection and deterministic checks'), distinguishing it from sibling tools that list, register, inspect, or run checks. It does not explicitly name alternatives but the purpose is clearly stated.
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?
No explicit when-to-use or alternative guidance is provided, but the phrase 'from a new live inspection and deterministic checks' implies it should be used after running an inspection and checks, giving some implied usage context. It does not reference sibling tools or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate task-oriented multi-tool journeys using sampling, with an explicit fallback.
| Name | Required | Description | Default |
|---|---|---|---|
| objective | Yes | Behavior or user outcome that generated scenarios must test. | |
| target_id | Yes | Identifier returned by register_target. | |
| maximum_scenarios | No | Maximum number of scenarios to generate. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, covering the safety profile. The description adds behavioral context like 'using sampling' and 'explicit fallback,' but does not elaborate on what the fallback is or when sampling is used, so the added transparency is limited.
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, front-loaded sentence with no redundant information. It efficiently conveys the core action and distinguishing traits.
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?
With an output schema present, return values are already covered. The description is minimal but does not mention what the explicit fallback is or how this tool fits into the overall workflow (e.g., after register_target). While not incomplete, it leaves important workflow context implicit.
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%, with clear descriptions for all three parameters. The description itself adds no parameter-specific meaning, so it does not exceed the baseline.
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: to generate task-oriented multi-tool journeys (scenarios) using sampling, with an explicit fallback. It uses a strong verb ('generate') and a specific resource, distinguishing it from sibling tools like list_targets or inspect_target.
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?
No explicit when-to-use or alternative guidance is provided. The description does not mention that target_id must come from register_target, nor does it contrast with run_static_checks or run_target_tool. Usage context must be inferred entirely from the schema.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Connect to a target and capture its complete paginated model-facing MCP contract.
| Name | Required | Description | Default |
|---|---|---|---|
| target_id | Yes | Identifier returned by register_target. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, and the description adds the paginated nature and completeness of the response, but provides no further behavioral details like network requirements or potential size limits.
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?
A single succinct sentence that captures the tool's function without extraneous information.
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?
With a clear schema for the one parameter, an output schema, and annotations covering safety, the description is sufficient for an agent to understand the tool's scope and behavior.
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 description itself does not elaborate on target_id, but the input schema provides a clear description ('Identifier returned by register_target'), so schema coverage is 100% and the description adds no additional parameter semantics.
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 uses a specific verb 'capture' with resource 'target' and specific output 'complete paginated model-facing MCP contract', clearly distinguishing from sibling tools like run_target_tool or list_targets.
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 after registering a target (target_id from register_target) to retrieve its contract, but it does not explicitly state when to prefer this over run_target_tool or other siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List registered targets without resolving or exposing environment values.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and destructiveHint, and the description adds valuable behavioral context by stating that environment values are not resolved or exposed, reinforcing the safe, read-only nature beyond what the annotations provide.
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, front-loaded sentence that is concise and complete, earning every word with no 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 parameterless listing tool with an output schema, the description fully covers the core behavior and key safety constraint. The output schema handles return value details, so no further explanation 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?
The tool has zero parameters and the schema coverage is 100% (empty schema). Baseline for 0 params is 4, and the description adds no additional parameter semantics needed.
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?
Description uses a specific verb ('List') and resource ('registered targets'), and adds a distinguishing qualifier ('without resolving or exposing environment values') that differentiates it from sibling tools like inspect_target.
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 phrase 'without resolving or exposing environment values' provides clear context for when to use this tool—when a safe, non-sensitive listing is needed—and implies alternatives for value resolution, though it doesn't explicitly name them.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Measure serialized model-facing tool metadata and exact duplicate descriptions.
| Name | Required | Description | Default |
|---|---|---|---|
| target_id | Yes | Identifier returned by register_target. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds that it measures serialized tool metadata and duplicate descriptions, but doesn't disclose output format, failure modes, or any side effects. Beyond annotations, it adds modest context.
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 concise sentence that front-loads the verb and object. Every word contributes to meaning; there is no redundancy or irrelevant detail.
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 has only one parameter, good annotations, and an output schema (not shown), the description is adequate but not rich. It lacks usage context and fails to explain what 'serialized model-facing' and 'exact duplicate descriptions' mean, which would help an agent understand the tool's role in the workflow.
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 coverage is 100%: target_id is described as 'Identifier returned by register_target.' The description does not add parameter-level detail beyond the schema, so the baseline of 3 applies.
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 action ('Measure') and the specific resource ('serialized model-facing tool metadata and exact duplicate descriptions'). This distinguishes it from siblings like list_targets and inspect_target, which perform different actions.
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 registered target) or situations where this tool is preferred over inspect_target or list_targets.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Register a secret-free stdio or Streamable HTTP target after boundary validation.
| Name | Required | Description | Default |
|---|---|---|---|
| cwd | No | Existing stdio working directory inside MCP_QA_ALLOWED_ROOTS. | |
| url | No | Streamable HTTP MCP endpoint; remote hosts require explicit opt-in. | |
| args | No | Argument vector for the stdio executable, without shell expansion. | |
| name | Yes | Human-readable name used to identify the target in reports. | |
| command | No | Executable for a stdio target; passed directly without a shell. | |
| env_names | No | Environment variable names to inherit; values are never stored. | |
| transport | Yes | Connection transport: stdio or streamable-http. | |
| header_env | No | Map HTTP header names to inherited environment variable names. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate non-read-only and non-destructive behavior. The description adds useful context by stating 'secret-free' (no secret handling) and 'after boundary validation' (safety checks), which go beyond the annotations. However, it does not disclose other behavioral traits such as persistence, duplicate handling, or side effects, leaving some transparency 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, front-loaded sentence that immediately conveys the tool's purpose. Every word is meaningful, with no redundancy or unnecessary detail. It is appropriately concise for the tool's complexity.
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 has 8 parameters and an output schema, the description provides minimal but adequate context. It covers the core action and key qualifiers but does not explain what 'boundary validation' entails or what the tool returns. The output schema likely covers return values, but the description could be more complete regarding prerequisites and expected behavior.
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%, with each parameter already having a detailed description (e.g., 'values are never stored' for env_names). The tool description does not add new parameter semantics beyond the mention of 'stdio or Streamable HTTP', which matches the transport parameter. Baseline 3 is appropriate given the 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 action ('Register'), the resource ('target'), and the scope ('stdio or Streamable HTTP'), with a specific qualifier ('secret-free', 'after boundary validation'). This distinguishes it from sibling tools like list_targets, inspect_target, and run_target_tool, which operate on already-registered targets.
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 verb 'Register' implies this is for creating a new target, and the phrase 'after boundary validation' hints at a prerequisite. However, there is no explicit guidance on when to use this tool vs. alternatives, no mention of exclusions, and no reference to sibling tools. Usage is implied but not clearly articulated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Analyze live schemas, descriptions, annotations, and size without tool calls.
| Name | Required | Description | Default |
|---|---|---|---|
| target_id | Yes | Identifier returned by register_target. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only and non-destructive behavior. The description adds the key behavioral guarantee 'without tool calls', clarifying that no target tool is executed, and 'live' indicates it reads current state. This adds useful context beyond annotations without contradicting them.
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?
A single sentence, front-loaded with the action verb, and contains no filler, redundancy, or extraneous information.
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?
With one fully-documented parameter, an output schema, and safety annotations, the description covers the essential behavior of static analysis. It is sufficient for the tool's low complexity, though it could mention the relationship to sibling tools like inspect_target for greater 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?
The input schema fully documents the single parameter target_id, and schema description coverage is 100%. The description adds no extra parameter semantics, so the baseline score of 3 applies.
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 uses the specific verb 'Analyze' and names concrete resources: 'live schemas, descriptions, annotations, and size'. The clause 'without tool calls' distinguishes it from siblings like run_target_tool, clearly positioning it as a static inspection tool.
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 phrase 'without tool calls' provides clear context for using this tool when non-invasive inspection is needed, implicitly contrasting with execution tools. However, it does not explicitly name alternatives or when-not-to-use conditions, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Execute one call; non-read-only tools require explicit side-effect approval.
| Name | Required | Description | Default |
|---|---|---|---|
| arguments | Yes | Reviewed JSON arguments passed unchanged to the target tool. | |
| target_id | Yes | Identifier returned by register_target. | |
| tool_name | Yes | Exact tool name from the target's live tool list. | |
| allow_side_effects | No | Explicit approval for tools not annotated read-only. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds value by disclosing the approval requirement for non-read-only tools, complementing the annotations (readOnlyHint=false, destructiveHint=true). It does not contradict the annotations and provides behavioral context about the execution guard.
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, well-structured sentence that immediately communicates the action and the critical approval condition. It is concise without unnecessary elaboration, earning a top score.
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 presence of an output schema and thorough input schema, the description covers the essential safety aspect. However, it lacks broader context about the tool's role in the workflow (e.g., registering targets first) and potential failure modes, making it minimally complete but not rich.
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 input schema already covers all parameters (100% coverage), including target_id, tool_name, arguments, and allow_side_effects. The description does not add parameter-specific semantics beyond what the schema provides, so baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Execute one call' clearly states the core action of running a single target tool invocation. It effectively distinguishes this as the execution step among sibling tools like inspect_target and run_static_checks, though it could more explicitly mention the target tool context.
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 an explicit rule: non-read-only tools require explicit side-effect approval. This gives conditional guidance on when to set allow_side_effects, but it does not describe when to use this tool versus siblings or mention workflow dependencies like registering a target first.
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.
v0.1.0build_reportgenerate_scenariosinspect_targetlist_targetsmeasure_context_costregister_targetrun_static_checksrun_target_toolMost tools have clearly distinct purposes (listing, registering, inspecting, checking, measuring, generating, executing, reporting). The only potential confusion is between run_static_checks and measure_context_cost, but their descriptions clarify that one focuses on quality/size analysis and the other on serialized metadata cost and duplicate descriptions.
All tool names follow a consistent verb_noun pattern (list_targets, register_target, inspect_target, run_static_checks, measure_context_cost, generate_scenarios, run_target_tool, build_report). The naming is uniform and predictable.
Eight tools is an appropriate scope for a QA lab. The set covers the main workflow areas without being bloated or sparse, aligning well with the typical 3-15 tool sweet spot.
The toolset covers the core workflow: register, inspect, run checks, measure cost, generate scenarios, execute a tool, and build a report. A minor gap is the absence of a delete/unregister target operation, which could make target lifecycle management incomplete.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Pre-execution governance for AI agents. Deterministic PASS/FAIL/REVIEW verdicts, replayable proof.
MCP server for building and testing AI agents with multi-model experimentation and insights.
Zero-secret MCP gateway for AI agents: risk-scored, audited calls with human-in-the-loop approval.
Security firewall for AI agents — scans MCP calls for injection, secrets, and risks.
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