mcp-test-runner
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-test-runnerrun pytest on tests/ and show coverage"
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.
mcp-test-runner
MCP server for deterministic local test execution and normalized test result reporting.
mcp-test-runner is the verification layer for codebase onboarding agents. It exposes focused MCP tools for running pytest and Jest, parsing test output, and summarizing coverage so downstream agents can mark claims as verified, unverified, or contradicted by real execution.
Codebase Onboarding Stack
mcp-test-runner is the verification layer in a three-server MCP tool stack for Project 6 wayfinder, a codebase onboarding agent.
mcp-repo-mappermaps repository structure, languages, entry points, framework evidence, and Python dependency edges.mcp-ast-explorerprovides symbol-grounded Python definition, signature, reference, call-chain, and class-hierarchy lookups.mcp-test-runnerruns local pytest/Jest checks and coverage summaries so agent claims can be verified against execution.
In wayfinder, this server turns high-risk code understanding claims into verified, unverified, or contradicted evidence from real test execution.
Related MCP server: MCP Testing Assistant
Status
This repository is a Python-first v1 MCP test runner. It supports bounded pytest execution, Jest command execution, single-test targeting, pytest coverage summaries, and normalized pytest/Jest JSON parsing.
The server does not use an LLM. Test results come from subprocess execution and structured parser output.
Tools
Tool | Purpose |
| Returns |
| Run pytest in a bounded working directory and return raw command output. |
| Run Jest through |
| Run one pytest node id or one Jest test-name pattern. |
| Normalize test runner JSON output. |
| Return pytest-cov JSON coverage totals. |
Supported Scope
Python 3.11+ package.
FastMCP 2.x server.
pytest execution with JSON report output via
pytest-json-report.Jest command execution via
npx jest --json --outputFile.pytest and Jest JSON normalization into one
TestRunResultschema.Single pytest node id execution and Jest test-name targeting.
pytest-cov coverage summary from
.coverage.json.Subprocess timeout plus POSIX
resource.setrlimitCPU / memory caps.
Current Limitations
v1 does not use Docker. It uses cwd validation, subprocess timeouts, and POSIX resource limits.
Resource limits require a POSIX platform that supports
resource.setrlimit.Jest execution expects Node.js plus project-local or
npx-resolvable Jest.Jest single-test targeting uses
--testNamePattern; it does not parse Jest file-specific node ids.Coverage summary is pytest-only in v1.
Tools return raw command output for execution; call
parse_test_outputto normalize framework JSON.
Local Development
Install dependencies:
uv sync --extra devThe PyPI distribution name is lovranran-mcp-test-runner because mcp-test-runner is already taken on PyPI. The installed console script remains mcp-test-runner.
Run the MCP server:
uv run mcp-test-runnerRun verification:
uv run ruff check .
uv run mypy
uv run pytestLicense
MIT
Available Tools
6 toolsget_coverage_summaryD
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | ||
| framework | No | pytest | |
| memory_mb | No | ||
| cpu_seconds | No | ||
| timeout_seconds | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| framework | Yes | |
| report_path | No | |
| total_lines | Yes | |
| covered_lines | Yes | |
| percent_covered | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no 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?
Tool has no description.
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?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
healthD
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no 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?
Tool has no description.
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?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
parse_test_outputD
| Name | Required | Description | Default |
|---|---|---|---|
| stdout | Yes | ||
| framework | No | pytest |
Output Schema
| Name | Required | Description |
|---|---|---|
| failed | No | |
| passed | No | |
| skipped | No | |
| failures | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no 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?
Tool has no description.
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?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_jestD
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | ||
| memory_mb | No | ||
| cpu_seconds | No | ||
| test_filter | No | ||
| timeout_seconds | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| stderr | Yes | |
| stdout | Yes | |
| exit_code | Yes | |
| timed_out | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no 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?
Tool has no description.
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?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_pytestD
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | ||
| memory_mb | No | ||
| cpu_seconds | No | ||
| test_filter | No | ||
| timeout_seconds | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| stderr | Yes | |
| stdout | Yes | |
| exit_code | Yes | |
| timed_out | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no 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?
Tool has no description.
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?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_single_testD
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | ||
| test_id | Yes | ||
| framework | No | pytest | |
| memory_mb | No | ||
| cpu_seconds | No | ||
| timeout_seconds | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| stderr | Yes | |
| stdout | Yes | |
| exit_code | Yes | |
| timed_out | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no 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?
Tool has no description.
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?
Tool has no description.
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.
6 tool updates
v0.1.0- First observed
get_coverage_summary - First observed
health - First observed
parse_test_output - First observed
run_jest - First observed
run_pytest - First observed
run_single_test
TDQS
Scored across 6 tools
The tool names clearly differentiate between different testing frameworks (pytest vs jest), single test execution, output parsing, coverage, and health. Without descriptions, there is slight potential confusion between run_pytest and run_single_test, but names are sufficiently distinct.
Three tools use the 'run_' prefix (run_pytest, run_jest, run_single_test), but parse_test_output, get_coverage_summary, and health deviate. The pattern is mixed but still readable and not chaotic.
With 6 tools, the server covers essential test runner operations: running tests by framework, running a single test, parsing output, coverage, and health check. Each tool serves a distinct purpose without unnecessary bloat.
The tool set covers core testing workflows: running tests (multiple frameworks), analyzing results (parse output, coverage), and health monitoring. Minor gaps like a combined test runner or report generation exist but do not hinder basic usage.
Maintenance
Resources
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