MCP tools for AI coding agents

Test Maze gives your coding agent 55 Model Context Protocol (MCP) tools to plan features, keep test cases, record test runs against the exact git commit and get a deterministic pass/fail verdict — plus exploratory testing, coverage gap analysis, regression baselines and code-quality checks. You ask in plain English; your agent picks the tools.

  • 55 tools in 15 groups
  • 13 readable resources
  • 8 ready-made prompts
  • Works with any MCP client

Sessions (progress tracker)

A session is an optional tracker for one piece of work as it moves plan → write tests → code → verify → ship. Read where a session is and what it expects next.

Test cases

A test case is one check of your app: steps to follow and the result you expect. Create, find, update, archive, delete or waive them here.

testcase.get

Reads one test case in full: title, description, pre-condition, steps (in order), expected result, post-condition, priority, status, acceptance-criterion label, waiver details (isWaived, waivedReason, waivedBy, waivedAt), severity, automationStatus, the store…

Try: “Show me the steps of the checkout test”

Get one test case — inputs and examples →

Create a test case

Changes data
case.create

Creates one test case in the connected workspace: a written check of the app with steps to follow and the result to expect, for example "Guest can check out without an account".

Try: “Write a test case for signing up with an email address”

Create a test case — inputs and examples →

Test runs

A test run records the results of running a set of test cases against a specific version of your code (git commit and branch). Create runs, record pass/fail per case and read them back.

testrun.get

Reads the summary of one test run in the connected workspace: { id, uri, name, buildVersion, description, startDate, endDate, status, gitSha, branch, workingTreeClean, releaseId, frozen, numberOfTestCases }.

Try: “How many tests were in yesterday's run?”

Get one test run — inputs and examples →

Product & connection

Your workspace describes one product: its vision, the problem it solves and who it's for. Check which workspace this connection is using, and read or update that description.

Features & user stories

Describe what you're building: features, smaller sub-features and user stories ("as a …, I want …, so that …"). Each is stored as a test suite that holds the test cases proving it works.

Test plans

A test plan is a named bundle of test cases you run together, like a smoke test before every deploy or a full regression pass before a release.

Create a test plan

Changes data
testplan.create

Creates a test plan: a named, reusable bundle of existing test cases you want to run together (for example a "Checkout smoke" plan with the five most important checkout cases).

Try: “Group the login and checkout tests into a smoke test plan”

Create a test plan — inputs and examples →

Releases

Group work into releases, sprints or milestones, then mark them shipped or cancelled.

Success metrics

Track whether the product and each feature actually succeed: product KPIs (e.g. weekly active users), per-feature metrics (e.g. checkout conversion) and the measurements recorded against them.

Media

Screenshots, PRDs and other files attached to test cases, runs and features.

Guided workflows

Start here. Each workflow tool kicks off a multi-step job (set up the product, plan a feature, turn a plan into tests, grade a run) and tells your agent exactly what to do next.

Coverage

Find what a feature is not tested for yet: happy path, edge cases, errors, permissions, accessibility, performance, browsers and unusual data.

Regression baselines

Lock in a fully passing run as the "must stay green" baseline. From then on, any case from it that breaks fails the verdict, so fixed things stay fixed.

Activity

Label what your agent is doing so it shows up clearly on the Agent Sessions page.

Exploratory testing

Let your agent click through your app in its own browser (for example with Playwright MCP) while Test Maze keeps a verified record, suggests what to try next and turns the journey into test cases. Test Maze never opens a browser itself.

Code quality

Two helpers for the code your agent writes: a code-smell check (fixed rules, same code gives the same result) and design advice that ranks proven design patterns and refactorings against your problem. Backed by a built-in knowledge base.

Resources your agent can read

Records an agent can open directly by address, like opening a file. Tool results link to them.

testmaze://product/{spaceId}
Your workspace as a product: vision, the problem it solves, who it is for, lifecycle stage and repository.
testmaze://feature/{spaceId}/{suiteId}
One feature: its lifecycle stage, the release it belongs to and the user stories under it.
testmaze://userstory/{spaceId}/{suiteId}
One user story ("as a …, I want …, so that …") and the acceptance-criterion test cases that prove it.
testmaze://case/{spaceId}/{caseId}
One test case: steps, expected result and whether it passed the last time it ran.
testmaze://testplan/{spaceId}/{planId}
One test plan and the test cases it bundles.
testmaze://testrun/{spaceId}/{runId}
One test run: pass/fail for each case plus the git commit, branch and whether the working tree had uncommitted changes.
testmaze://release/{spaceId}/{releaseId}
One release, sprint or milestone and its status.
testmaze://media/{spaceId}/{mediaId}
One uploaded file (screenshot, PRD, …): type, size and a public link.
testmaze://productkpi/{spaceId}/{kpiId}
One product KPI: what is measured, the unit, the target and whether higher or lower is better.
testmaze://featuremetric/{spaceId}/{metricId}
One feature success metric: baseline, target and where tracking stands.
testmaze://metric-observations/{spaceId}/{metricType}/{ownerId}
The measurements recorded over time for one KPI or feature metric.
testmaze://kb/{spaceId}/{kind}/{slug}
One knowledge base entry: a design pattern, code smell or refactoring (kind is pattern, smell or refactoring), with links to related entries.
testmaze://exploration/{spaceId}/{id}
One exploratory-testing session: every step with its verification, the pages visited, coverage and the navigation map.

Ready-made prompts

Step-by-step instructions stored in Test Maze that guide your agent through a job. In Claude Code they appear as slash commands such as /mcp__tm__feature-spec.

code-smell-review
Review code for smells and plan the refactoring. Follow-up to quality.smell_check.
architecture-review
Recommend a design pattern with a migration plan. Follow-up to architecture.advise.
explore-feature
Explore an app in your own browser and record it. Step-by-step guide for exploratory testing: you click through the app with your own browser tools (for example Playwright MCP) and Test Maze keeps the record; Test Maze never opens a browser itself.
exploration-to-cases
Tidy up test cases recorded while exploring. Optional follow-up to exploration.to_cases.
product-init
Describe the product from its repository. Used in step 1 of the loop, after project.initialize returns this prompt name.
feature-spec
Plan a feature as user stories and acceptance criteria. Used in step 2 of the loop, after feature.implement returns this prompt name.
ac-to-testcase
Write full test steps for acceptance criteria. Used in step 3 of the loop, after feature.verify returns this prompt name.
find-coverage-gaps
Propose test cases for missing kinds of testing. Follow-up to coverage.gap_for_feature.

Frequently asked questions

What is an MCP tool?

The Model Context Protocol (MCP) is an open standard that lets AI coding agents call external tools. Each Test Maze tool is a named action, such as case.create or pdlc.verify, that your agent can call with structured inputs and get structured results back.

Do I call these tools myself?

No. You describe what you want in plain English, for example "write test cases for the password reset flow", and your coding agent picks the tools and fills in their inputs.

Which coding agents can use Test Maze?

Any MCP client. Setup instructions in the app cover Claude Code, Cursor, Cline, Gemini CLI and Codex CLI.

How do I connect my agent?

Create an MCP token in your Test Maze workspace, run "npx -y @testmaze/mcp init <token>" in your project folder, then register the server with your agent, for example "claude mcp add tm --scope project -- npx -y @testmaze/mcp".

Does Test Maze read my source code?

No. Test Maze never clones or scans your repository. It only receives what your agent sends in a tool call: test cases, run results and git identifiers, plus a code snippet when you explicitly ask for a code-quality check.

Is the pass/fail verdict decided by AI?

No. pdlc.verify computes the verdict with fixed rules over the recorded results, so the same results always give the same verdict. AI may only add an optional hint about what to fix.