QA Engineering MCP Hub
Click on "Deploy 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., "@QA Engineering MCP Hubcheck the QA hub health and list its capabilities"
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.
QA Engineering MCP Hub
QA Engineering MCP Hub is a local-first, cross-platform FastMCP foundation for
governed QA workflows. Milestones 00 and 01 provide the MCP foundation and secure
local configuration control plane. All external providers and QA execution features
remain explicitly NOT_IMPLEMENTED or SCAFFOLDED.
What works through Milestone 01
qa-mcp servestarts a standalone FastMCP 4 server over stdio.hub_healthreports local process readiness.hub_capabilitiesseparates callable features from future work.qa-hub://statusis a read-only JSON MCP resource.qa_hub_orientationis a reusable MCP prompt.qa-mcp doctor,qa-mcp capabilities,qa-mcp --help, andqa-mcp --versionprovide local CLI diagnostics.Pydantic Settings validates non-secret
QA_MCP_environment configuration.Portable configuration paths and simulated Windows/macOS/Linux behavior are tested.
Versioned YAML supports named profiles, multiple connections per provider, repository/workspace references, execution preferences, and strict validation.
Credentials are referenced through an OS keyring or explicitly named environment variables; resolved values are redacted and never serialized.
setup,profiles,integrations,config, andclientscommands manage and inspect local readiness. Integration tests do not make provider network calls.Codex TOML and native VS Code JSON can be generated without provider credentials.
No provider adapter, provider OAuth exchange, repository or ticket read, test execution, database query, Kafka operation, CI/CD action, test-management action, evidence publication, or release approval is implemented.
Related MCP server: checkyourself
Prerequisites and setup
Install Python 3.12 or newer and uv, then run:
uv sync --frozen
uv run qa-mcp --help
uv run qa-mcp doctor
uv run qa-mcp capabilities
uv run qa-mcp setup
uv run qa-mcp config validateStart the server:
uv run qa-mcp serveA stdio MCP server waits for a client and normally appears silent. stdout is reserved for MCP protocol messages.
Codex registration
Use absolute paths for both uv and this repository:
codex mcp add qa_hub -- /ABSOLUTE/PATH/TO/uv run --project /ABSOLUTE/PATH/TO/qa-engineering-mcp-hub qa-mcp serve
codex mcp listCodex uses .codex/config.toml; native VS Code/Copilot MCP JSON is a separate
configuration dialect. See the Codex setup guide
and platform guides.
Architecture
The project deliberately separates:
the reusable framework in
src/qa_mcp_hub/;company-specific assets created from
templates/qa-workspace/;per-machine user configuration and credential references.
Read the canonical architecture,
decisions, and
scaffold manifest before structural
changes. Provider status and documentation are indexed in
docs/providers/README.md.
Validation
uv lock --check
uv run ruff check .
uv run ruff format --check .
uv run mypy src
uv run pytest -v
uv run python scripts/structure_audit.py
uv buildThe progress report records actual executed evidence. Simulated path tests do not constitute live Windows or Linux verification.
Contributing and security
See CONTRIBUTING.md, SECURITY.md, and AGENTS.md. Never commit credentials, customer data, local workspace content, or fabricated integration results.
License
QA Engineering MCP Hub is available under the MIT License.
Available Tools
2 toolshub_capabilitiesHub CapabilitiesB
Return implemented and planned capabilities without overstating support.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| planned | Yes | |
| implemented | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, but it does add one meaningful behavioral caveat: it returns both implemented and planned capabilities and should not overstate support. It does not state that the operation is read-only, side-effect free, or how 'planned' should be interpreted.
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?
One front-loaded sentence with no wasted words. The wording is efficient, though the phrase 'without overstating support' is slightly vague and could be sharper.
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?
Because an output schema exists, the description needn't explain return values, and with zero parameters the input side is trivial. It is nearly complete, though a read-only safety note and explicit sibling contrast would close the remaining gap.
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 takes zero parameters, which is the baseline-4 case. The schema is trivially complete and the description adds no contradictory parameter expectations.
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 gives a specific verb ('Return') and resource ('implemented and planned capabilities'), so an agent can tell this is a capability-discovery tool rather than a health probe. It does not explicitly name or contrast with the sibling hub_health, leaving differentiation partly to inference.
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?
There is no explicit when-to-use guidance, no mention of the hub_health alternative, and no stated preconditions. The usage is only implied by the word 'capabilities'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
hub_healthHub HealthA
Return local server readiness; do not probe future providers.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| status | Yes | |
| service | Yes | |
| version | Yes | |
| transport | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden; 'Return' implies a read-only check and the future-provider caveat hints at a side-effect-free scope, but auth requirements, cost, and behavior are unstated. The presence of an output schema relieves it of explaining return values, which keeps this at an adequate 3.
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 compact sentence with the action front-loaded and the constraint immediately after. Nothing is wasted, though the 'future providers' phrasing is cryptic enough to require interpretation.
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 zero-parameter read with an output schema, the definition covers what the tool does and one important scope limit. Only the meaning of 'future providers' and the relationship to hub_capabilities are left open.
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 takes zero parameters, so there is nothing for the description to disambiguate; baseline 4 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?
States a specific verb ('Return') and resource ('local server readiness'), so the agent knows this is a health/status check rather than a capability listing. The scope caveat ('local') helps separate it from hub_capabilities, though the sibling is never named and 'readiness' is slightly abstract.
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?
Provides one explicit exclusion ('do not probe future providers'), which is real when-not guidance. However, it never says when to prefer this over hub_capabilities or what 'future providers' means, so the routing guidance is only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
v0.1.0- First observed
hub_capabilities - First observed
hub_health
TDQS
Scored across 2 tools
hub_health (readiness/liveness) and hub_capabilities (feature inventory) are conceptually separable and the descriptions reinforce the split. They are still both metadata-only introspection calls, so an agent may occasionally grab one when it wanted the other.
Both tools use a clean, identical `hub_<noun>` snake_case pattern. There is no mixing of verbs, casing, or prefixes.
Two tools is very thin for something billed as a 'QA Engineering MCP Hub' — it is a stub surface rather than a scoped toolset. Both tools earn their place, but the count is far below what the stated purpose implies.
The surface covers only self-introspection; there are no QA-engineering operations at all (no test discovery, run, results, coverage, or reporting). An agent reaching for this hub for its advertised domain hits an immediate dead end.
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