Comprehensive Model Context Protocol server for managing Home Assistant through AI assistants.
Nothing to run from here
Aptora is installed from its publisher's own source and answers where it runs, so this marketplace is not in the path of a single call. There is no address here to send one to, and a panel that pretended otherwise would be showing you an answer we made up. Install it and call it from your own client — the Installation tab has the entry for each one.
92/100
trust score
6
downloads a day
local
runs on your machine
201
downloads in 30d
What it does
MCP server for the Aptora API: create assessments, invite candidates, pull grading results
Quickstart
# 1 — add it to Claude Code
claude mcp add --scope user --env 'APTORA_API_KEY=${APTORA_API_KEY}' --transport stdio aptora-mcp '--' npx -y @aptora/mcp
# 2 — ask your agent for something these tools do
# get_authoring_guide, get_dry_run_guide, whoami and 9 more
It needs APTORA_API_KEY: this reads it from your environment, so export it first.
Aptora costs nothing on mcprush: there is no plan to choose here, no cap to set and nothing mcprush can bill you for.
Publishernot claimed
TRAll 1 server
tryaptora1 server
1 category
Collected from a public index. Nobody has claimed this account, so nothing here was written by its author — claim it if it is yours.
Installation
Where are you running it?
Every route below installs the same thing and ends at the same approval screen. This one runs on your machine: your client starts Aptora as a process under your own user, with your files and your network, so the tool surface below is what it can do to your computer rather than to a server somewhere else. Its scan result is on the Security tab — read the surface before you approve it.
Claude Codedeclared by this listing
This is a public server: you run it yourself and this marketplace is not in the path. You need Node.js — the current LTS (24) is the safe choice; npx comes with it. Claude Code registers it in one command; --scope user makes it available in every project. Claude Code reads APTORA_API_KEY (API key from Integrations → API Keys in the Aptora dashboard) from your environment each time it starts the server, so the value stays out of its config: export it in your shell profile (macOS, Linux), or run setx APTORA_API_KEY … and open a new terminal (Windows). The '--' is in quotes so that PowerShell passes it on.
claude mcp add --scope user --env 'APTORA_API_KEY=${APTORA_API_KEY}' --transport stdio aptora-mcp '--' npx -y @aptora/mcpReconnect, or start a new session, and the tools appear in the model’s tool list.
Free on mcprush, and it runs from its publisher’s own package: this marketplace is not in the path, so there is no key of ours to hold and no card to add.
What it asks of you
It reaches a system of yours, so it needs your own credential rather than the publisher’s: APTORA_API_KEY — API key from Integrations → API Keys in the Aptora dashboard. You set it in your own client’s config, on your machine — this marketplace never holds it.
What it writes
One config entry your client uses to start the process locally. A local server runs with your file system and your network, which is why it is priced without metering.
What you approve
12 tools, with what each one reads, writes and reaches shown before you agree — the same list on every route above. Read the tool surface.
Tool surface
What the model actually sees. Descriptions are diffed on every release — see version history.
How to write an Aptora assessment: field meanings, the required ProseMirror problem format, environment constraints, setup hooks and black-box services, personas, and grading categories. Call before creating or editing an assessment.
No parameter schema on file.
Step-by-step QA of an assessment by taking it as a test candidate in a browser: files, setup hook, black-box services, personas, browser preview. Call before dry-running an assessment.
No parameter schema on file.
Return the Aptora user the configured API key belongs to. Use to verify the connection.
No parameter schema on file.
List all assessments in the user's organization.
No parameter schema on file.
Fetch one assessment by id, including its full configuration.
No parameter schema on file.
Create a new assessment. Call get_authoring_guide first. Pass workspace_directory to give candidates starter files. Dry-run it (get_dry_run_guide) before inviting anyone.
No parameter schema on file.
Update an existing assessment. Only the fields you pass change; everything else is preserved. Pass workspace_directory to replace the starter workspace.
No parameter schema on file.
Create an interview for a candidate WITHOUT emailing them. Returns the interview including its link, which you can share yourself. Also used for dry runs. Use invite_candidate to have Aptora send the invitation email.
No parameter schema on file.
Create an interview AND send the candidate a real invitation email immediately. Only use when the user explicitly wants the invitation sent.
No parameter schema on file.
List all interviews in the user's organization with status and grading summary (overall score, reviewed flag).
No parameter schema on file.
Fetch the full AI grading report for a completed interview: scorecard, timeline, metrics. Grading runs asynchronously after completion — the response reports in-progress status when not done yet.
No parameter schema on file.
Fetch the candidate's final workspace (file tree with contents) captured when the interview ended. Can be large.
No parameter schema on file.
Free
$0 forever
- Every tool, no call limit
- No card, and mcprush charges nothing for it
- Source published under MIT
- Runs on your machine — nothing of it reaches our gateway
- Nothing to cap, because mcprush bills nothing
What counts against your monthly calls
| Tool | Unit | Calls used | Out of the allowance |
|---|
Nothing here is billable: mcprush charges nothing to install Aptora or to call it, at any volume.
Call allowance · this installenforced at the gateway
Monthly allowance, in calls
—
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Alert at 80% · then the tool returns a normal MCP error
Used this month: —
ATrust score 92 / 100
Release 1.0.0 · scanned 14 Oct 2026
Two independent axes, because powerful and malicious are different questions. The grade is threat only. The capability level is blast radius, and it is never a penalty on the grade — it is priced as one subtract-only term in the score, where you can see it.
Threat score100
Client score92
Findings2 threat · 1 capability
Capabilityhigh
Coveragesource
Verificationnone
Surface digest7416a3bc1c2d6823c9dd5f2f4fd6778c7198ec43a8cf4aa726f357998895201b
Score vectorthreat 100 → client 92
| Term | Level | What it prices | Points |
|---|---|---|---|
| capability-exposure | high | capability blast radius (high) — client exposure if the model is manipulated | −6 |
| verification-discount | none | publisher verification (unlinked) — no provenance/repo link, but the shipped source was fully read | −2 |
| coverage-honesty | source | inspection depth (source) — how much of the target the scan could see | −0 |
score = clamp(round(threatScore − E_cap − E_ver − E_cov), 0, 100). No model, no opinion, no network call at scoring time: the same methodology version against the same bytes always produces this number, and every line above is a field of the report rather than a summary of it.
What the scan could actually read
A grade is only as meaningful as its coverage, so the scanner publishes its own depth before it publishes its result.
Tool surfaceread
Implementation sourceread
Package metadataread
Live transportnot available
Tools were statically extracted from the published source (12 recovered), not enumerated from a running server. Tool-poisoning, Unicode-smuggling, capability and toxic-flow analysis ran on this inferred surface, but a mis-parsed registration could be missed or mis-attributed, so tool-derived findings are capped below “confirmed”. To grade the real runtime surface, scan the running server: --command "npx -y <package>".
Capability — what it could do if the model were manipulated
Tags derived from each tool’s schema and the implementation, not from what the tool calls itself. high is the level these add up to.
| Tool | Capability tags | Why the tag was assigned |
|---|---|---|
| get_authoring_guide | no tags | |
| get_dry_run_guide | no tags | |
| whoami | no tags | |
| list_assessments | no tags | |
| get_assessment | untrusted-input | untrusted-input: keyword "fetch" |
| create_assessment | external-sink | external-sink: handler sends an HTTP POST ( client.post("/api/v1/assessments") at dist/index.js:2528, handler sends an HTTP PUT (fetch(presignedUrl) at dist/index.js:2438 |
| update_assessment | external-sink | external-sink: handler sends an HTTP PUT ( client.put(/api/v1/assessments/${id}) at dist/index.js:2549, handler sends an HTTP PUT (fetch(presignedUrl) at dist/index.js:2438 |
| create_interview | external-sink | external-sink: handler sends an HTTP POST ( client.post("/api/v1/interviews") at dist/index.js:2565 |
| invite_candidate | external-sink | external-sink: handler sends an HTTP POST ( client.post("/api/v1/interviews/send-invitation") at dist/index.js:2583 |
| list_interviews | no tags | |
| get_grading_results | untrusted-input | untrusted-input: keyword "fetch" |
| get_workspace_snapshot | untrusted-input | untrusted-input: keyword "fetch" |
Toxic-flow graph
The lethal trifecta, checked as a graph rather than as a checklist: untrusted input, a sensitive source and an external sink have to meet before there is a path worth worrying about.
flow-1
Untrusted input can drive an external action even though no sensitive source is exposed.
Supply chain and provenance
Integrityfirst-seen
Surface digest7416a3bc1c2d6823
Scanned2026-10-14
This is the first scan of this surface here, so there is nothing yet to compare it against.
Every result on this tab comes from one deterministic pass over the published package — offline, rule by rule, and auditable line by line above. Same methodology version, same bytes, same score.
Release history
Always latest — the install command names no version, so it installs the newest release when it runs; the current release on file is 1.0.0. Pin a release below and the command asks for exactly that one.
1.0.0
undated
latestNo release note was published with this version.
—
no reviews yet
Who can review
One review per account, from a signed-in account that does not publish this listing. Nothing else is asked — you can say what you think before you install it. Publishers can reply once per review, and a review can be edited or deleted by whoever wrote it.
Sign in to write one
Any signed-in account that does not publish Aptora can review it, once — before installing it or after. A review from an account that has installed it is marked as one.
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Nobody has reviewed this listing. The rating on the card is the mean of the reviews written here and nothing else, so there is no rating until somebody writes the first — which takes a signed-in account that does not publish it, and nothing else.