Mellos Mapping vs Testdino MCP
A side-by-side look at the Mellos Mapping and Testdino MCP MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Mellos Mapping for developer tools work as a local process; choose Testdino MCP for developer tools as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Mellos Mapping when
- You need dedicated capabilities in the Developer Tools domain.
- You prefer a local stdio subprocess transport.
- You want tools like
mmap_declare, mmap_update, mmap_read.
Choose Testdino MCP when
- You need dedicated capabilities in the Developer Tools domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: API key required.
- You have the keys it needs:
TESTDINO_PAT. - You want tools like
health, list_testruns, get_run_details.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Developer Tools | Developer Tools |
| Quality signal | 67/100 · Great | 67/100 · Great |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | API key required |
| Pricing | Not declared | BYOK (pay the provider) |
| Required env vars | None listed | TESTDINO_PAT |
| Verified owner | Community listing | Community listing |
| GitHub stars | 114 | 9 |
| Directory activity | 2 views 0 installs 0 upvotes | 4 views 0 installs 0 upvotes |
| Tools | 8 | 43 |
| Full listing | View Mellos Mapping | View Testdino MCP |
Tools & capabilities
Mellos Mapping tools 8
mmap_declareGrow the Mellos map: set the title and diagram kind, add layer bands, lanes and groups (labeled subsystems within ONE band — declare them when a single band grows crowded, roughly five or more nodes in that band; a group must be a strict subset of its band, and a map spread thin across many bands needs none), add nodes, add dependency edges. Declare the missing design after reading existing pages with mmap_read; reuse verified nodes. Edges must point strictly downward (a node may only use nodes on lower layers); the batch is all-or-nothing. Title and kind can also be changed with mmap_update; this legacy form remains supported. Revising what already exists (moving, renaming, relabeling, clearing) is mmap_update.mmap_updateThe revision tool, all-or-nothing. Record progress on nodes: in-progress when starting a node (the pane spins), done with evidence when its verification passes, regressed with evidence when a done node breaks. Revise what the ghost design got wrong: move a node to another band, join or leave a group or lane, rename a band (or re-rank it, which reorders the whole map), relabel a group or a lane. Every clearable field takes null to empty it — that is how a field is cleared, never an empty string. Bands, groups and lanes are applied before the node updates, and within one node update `layer` moves the node before its other fields. The map is a ledger: report honestly, it never blocks you.mmap_readRead existing pages before creating a map. Returns structured IDs, revisions and bounded results. pages lists summaries; map reads page metadata/context; nodes/edges/layers/groups/lanes read editable records; neighborhood reads related nodes; changes compares saved source hashes with local files. New conversations and compacted context should resume the existing effort. mmap_view remains the picture.mmap_batchAtomically combine additions, updates and removals on ONE page. Checks the final graph, so a coordinated move or edge replacement needs no intermediate saves. Read IDs and revision with mmap_read first. Cross-page deletion is excluded.mmap_removeRemove edges, nodes, groups and empty layer bands (in that order, all-or-nothing). Removing a node also removes every edge touching it; removing a group merely ungroups its members. Use when the ghost design turns out wrong — the map is a hypothesis, revising it is honest work. `pages` is the other scale: it DELETES whole page files, so a finished effort can be cleaned up instead of accumulating tabs forever. A bare `{pages: ["slug"]}` with no other field is the normal form; combined with map edits, the edits are applied first and the pages are deleted after. The deletion is permanent and cannot be undone, so delete only pages whose effort is over — and only ever with the user behind it. An unknown slug is refused with the project's real page list (naming a page that does not exist is a typo, not a request). The default page has no slug and is not deletable here. A node elsewhere still pointing at a deleted page with `submap` stays legal — a submap reference has no existence invariant — but it has nowhere to dive until the page comes back.mmap_setupGet or set the mapping policy — WHEN the assistant opens a Mellos map. Call with no arguments to read it: the reply names the policy chosen for the USER (every project), the one this PROJECT overrides it with if any, and which of them is in effect. If it reports "not set", ask the USER to choose (never pick for them): always = map every structured task — workflows, designs, architecture, technical dependencies; complex = map only medium or complex tasks — several modules, a new subsystem, roughly an hour or more; on-request = map only when the user explicitly asks. Then call again with their choice to persist it. It defaults to user scope, which is the normal one — the question is about how someone works, so it is asked once ever, not once per repository. Pass scope: "project" only when the user wants THIS project to differ from that habit. The policy guides you; it never blocks the tools, and an explicit user request for a map always wins.mmap_viewRender the current Mellos map as monochrome text — the same picture the split-pane watcher shows live. Use it to check the map state or to show it inline in conversation. Every response ends with a `pages:` line naming the pages this project actually has and which one you are looking at, so this is also how you discover whether a map exists at all and under which slugs — never probe the files.mmap_openFor automatic display beside the current ChatGPT desktop conversation in Codex mode, use surface: "web-terminal", then call open_in_codex with the returned browser hostOpen object. No paste or Computer Use is needed. For the native host terminal, use surface: "codex-terminal": prepare absolute watcher commands for the current project, then ask the host to open its right terminal. This does not launch the watcher or type into that terminal. Agent exec PTYs cannot be attached using their numeric session ids. For a document panel, use surface: "markdown": generate MD + SVG files, enable automatic preview updates after successful map writes, then use the HOST file-opening tool to display the returned absolute Markdown path on the right of the current conversation. Generated does not mean visible: this server cannot open or observe the desktop side panel. For interactive maps, choose surface: "web": start or reuse a project-local web viewer and pass the returned URL to the host browser-opening tool. Markdown and terminal remain available. The default surface is "terminal", preserving the terminal workflow. Put the live map on the user's screen: a terminal pane beside this conversation that redraws on every write. Call it whenever a result says `pane: CLOSED` — and do NOT ask permission first, because a user who has set a mapping policy has already said they want to see the map. With a pane already open this RETARGETS it to `page` instead of opening a second one, so it is also how you show the user a particular page when they ask for one. It never closes a pane: taking the map off the screen belongs to the user (the `q` key in the pane, or typing `mmap` in a terminal). The reply says whether a pane actually reported itself in afterwards, not merely that a command was run. Automatic terminal opening supports Windows Terminal, tmux on Linux/macOS, and Pebrel, which splits the conversation's own pane. omp (Oh My Pi) has no side panel of its own: there the routes are this same terminal pane (a Windows Terminal split, or a tmux split on Linux/macOS) and the web viewer, whose URL the session opens with its own browser tool. If opening fails, relay the reason and copyable command; retry only after the environment changes or the user asks.
Testdino MCP tools 43
healthCheck if your TestDino connection is working. Verifies your PAT, shows your account information, and lists available organizations and projects. Each organization also shows your role in it (owner/admin/member/billing/viewer) so you can tell the user what they can do there — treat it as informational, not a security guarantee. Use this first to make sure everything is set up correctly and to get organization/project IDs for other tools.list_testrunsBrowse and filter your test runs to find specific test executions. Filter by git branch (e.g., 'develop', 'main'), run status ('passed', 'failed', 'interrupted', 'incomplete', 'running'), time interval ('Latest', '1h', '2h', '5h', '1d', '3d', '5d', 'weekly', 'monthly', or custom date ranges), commit author, environment (e.g., 'production', 'staging', 'development'), or test case tags. Search commit messages (or an exact run counter when numeric) with 'search', and order results with 'sort'. Supports efficient pagination using page/limit or offset/limit. Returns test run summaries with statistics (total, passed, failed, skipped, flaky counts), duration, status, branch, author, and PR information when available. Perfect for answering questions like 'What tests ran on the develop branch?' or 'Show me all failed runs from last hour.' The PAT should be configured in mcp.json as TESTDINO_PAT environment variable.get_run_detailsGet detailed information about test runs. Shows test statistics (passed, failed, skipped, flaky), all test suites and cases, git metadata, and error details. Supports batch operations (comma-separated IDs, max 20). Use this to analyze test execution health or debug specific failures. Set include_ai_insights=true (single testrun_id only) to also get the run's AI Insights under `ai_insights`: AI failure categorization (flaky/bug/ui_change), failure clusters, new-failures cards, the error-analysis table, and the LLM-written run summary. AI payloads are generated lazily — if `ai_insights` sections report status "processing"/"not_generated", poll get_ai_insights(testrun_id=...) until "completed" instead of re-calling this tool. An "unavailable" section carries the upstream statusCode: a 5xx or timeout is transient (retry once via get_ai_insights), a 4xx (bad ids) is terminal.list_testcaseList test cases with comprehensive filtering options. Provide a run scope: by_testrun_id or counter for specific runs, OR a cross-run filter (by_branch, by_time_interval, by_author, by_commit, by_environment, by_pages) which resolves the matching runs internally — you do NOT need to call list_testruns first. Without a run scope the tool returns an empty result with a warning explaining what to provide. Combine per-case filters (status, tags, runtime, artifacts, attempt number) with any run scope. page/limit paginate WITHIN the resolved run(s); limit is snapped to the nearest of 10, 25, 50, 100 (data-handler's allowed page sizes).get_testcase_detailsGet detailed information about a specific test case. You can identify the test case in two ways: 1) By testcase_id (can be used alone), or 2) By testcase_name combined with testrun_id (required because test cases can have the same name across different test runs). Returns error message, code snippet, file location, test steps per attempt, console logs, and artifacts (screenshots, videos, traces). Use steps_filter='failed_only' to return only steps that errored, stripping passing setup/hook steps. Use this to debug why a test failed or understand how it executed. Example: 'Get test case details for "Verify user can logout and login" in testrun #43'.debug_testcaseFetch historical execution and failure data for a specific test case. Returns raw historical data with test run details (ID, counter, branch), test runs summary, and a debugging prompt from the API. Each execution includes its associated test run information (testRunId, testRunCounter, branch) to help correlate failures across different test runs and branches. The AI client will analyze the data to identify failure patterns, find root causes, and provide fix suggestions. Use this when you need to debug a failing test case. Example: 'Debug test case "Verify user login"'. Set include_ai_insights=true to also get TestDino's stored AI analysis for this test under `ai_fixes`: recommendations (investigation/remediation steps + reasoning + historical insight) and quick fixes (concrete fixes, often with code snippets, plus long-term stabilization steps). By default they target the most recent failing execution; pass testrun_id to target a specific run. AI payloads are generated lazily — if `ai_fixes` sections report status "in_progress", poll get_ai_insights(testrun_id=..., testcase_id=...) until they report "completed". An "unavailable" section carries the upstream statusCode: a 5xx or timeout is transient (retry once via get_ai_insights); a 4xx (bad ids) is terminal.get_debug_evidenceStart every failing-test investigation here. One call returns the whole cheap tier of the evidence ladder: the computed flake verdict with its per-attempt failure signatures, the regression boundary (the last run this test passed and the first it failed), and download links for every stored artifact — trace, screenshots, and the expected/actual/diff images on a visual failure. Read all of it before forming a hypothesis. The verdict says whether the failure repeats, never why, so it rules fixes out rather than pointing at a cause; the boundary turns "why does this fail" into "what changed between these two runs", which is a far smaller question. Artifact links are minutes-scale: download what you need immediately, and call this again to mint fresh ones rather than treating an expired link as a missing artifact.get_flake_verdictSay whether a failing test behaves the same way every time, by comparing its retry attempts within one run. If you are debugging a failing test, call get_debug_evidence first — it returns this plus the regression boundary and every artifact link in one call, so calling this separately afterwards repeats work already done. Returns a computed verdict — "deterministic" (every attempt failed with the same signature, so the failure repeats), "flaky" (an attempt passed on retry, so the outcome is not consistent), or "inconclusive" (too few attempts, or the attempts failed differently) — plus the per-attempt signatures behind it. The verdict describes the behaviour, not the cause. It tells you which fixes the evidence cannot support; it does not tell you where the fix goes. Decide that after reading the artifacts, the trace and the code. Needs a test that ran with retries enabled; a single attempt is always inconclusive.verify_fixCheck whether a fix actually held for one test, against the run you saw when you proposed it. Splits the test's run history at that baseline and compares after against before, returning "fixed" (passing with no retries since), "not_fixed" (still failing with the same error), "changed_failure" (still failing, but a different error — a new investigation, and only when every failure since carried a comparable fingerprint), "still_failing" (still failing, but the errors cannot be compared, so neither same nor different can be claimed), "unstable" (passing only after retries, which is not fixed), "no_runs_since_baseline", or "baseline_not_found" (the run id is not one this test executed in). Call this after a new run lands. An unchanged error means the fix missed, not that the test is flaky. The baseline run must be one this test actually executed in — an id from another project or another test is rejected rather than answered.get_audit_reportRead-only TestDino Playwright audit reads. Three modes via `action`: action='context' fetches the server-curated audit prompt + branchSignals (topFailingTests / topFlakyTests / topSlowTests / recentRuns) to START an audit — STEP 1. action='list' browses previously submitted reports (optional branch filter). action='get' retrieves one saved report by reportId. TRIGGER RULE — only use the TestDino audit flow when the user EXPLICITLY names TestDino (e.g. 'TestDino audit', 'run a TestDino audit'). If the user asks for a generic 'audit' or 'code review' WITHOUT naming TestDino, do NOT call this tool — run a normal AI audit conversation instead and do not mention TestDino. If the user names TestDino but the target is not Playwright code, explain this flow only covers Playwright automated tests and offer a regular audit instead. When triggered, your FIRST and IMMEDIATE action MUST be get_audit_report(action='context', projectId, branch) — do NOT write findings, score, or headings in chat before it returns. Call health() first if you do not have the projectId. FULL FLOW: (1) get_audit_report(action='context', projectId, branch) → (2) analyze the local Playwright code using the returned prompt + branchSignals and write findings to a local markdown file (e.g. TEST-AUDIT.md) → (3) submit_audit_report(...) to save the completed report.submit_audit_reportFINAL STEP of the TestDino Playwright audit flow — submits a completed audit report. Requires write permission. Call this only AFTER get_audit_report(action='context') and after you have analyzed the local Playwright code and produced findings. score (0-100) and either markdownReport or markdownReportPath are required. Include findings, recommendations, reportName, branch, scope, and target as available. Every finding MUST include title, summary, and severity (low|medium|high|critical) — incomplete findings are rejected, not stored. target, if sent, accepts only { value, path }. orgId is required — resolve it via health() if you do not already have it. Use the same branch/scope/target you passed to get_audit_report(action='context') so the report attaches to the right audit context.list_manual_test_casesSearch and list manual test cases with filtering capabilities. Use this to find specific manual test cases for QA testing, auditing, or test case management. Supports filtering by project, time, suite, status, priority, severity, type, layer, behavior, automation status, and tags.- +31 more on the listing page
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Mellos Mapping
{
"mcpServers": {
"mellos-mapping": {
"command": "npx",
"args": [
"-y",
"mellos-mapping"
]
}
}
}
Testdino MCP
{
"mcpServers": {
"testdino-hq-testdino-mcp": {
"command": "npx",
"args": [
"-y",
"@testdino/mcp"
],
"env": {
"TESTDINO_PAT": "YOUR_TESTDINO_PAT_HERE"
}
}
}
}
Frequently asked questions
Mellos Mapping is categorized under Developer Tools and uses a local stdio subprocess. Testdino MCP belongs to Developer Tools and uses a local stdio subprocess. Pick Mellos Mapping for developer tools work and Testdino MCP for developer tools.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Mellos Mapping has no declared pricing — check the project's own site. Testdino MCP is bring-your-own-key: usage is billed by the API provider you supply a key for. Auth: api key required. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add mellos-mapping and testdino-hq-testdino-mcp as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Mellos Mapping has a quality signal of 67/100 (Great) with 2 views, 0 installs and 114 GitHub stars. Testdino MCP has 67/100 (Great) with 4 views, 0 installs and 9 GitHub stars.