Ticket AI
PYPI · TICKET-AI-MCP · SCANNED SEP 20
Measures tickets against the ones your team already shipped: sections, length, labels, duplicates.
Available components
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security44
- Malware scan not yet available for this package.Unverified
- No known CVEs affecting this package version or its production dependencies.Pass
- Runs a script at install time (build_backend) that we could not recognise. It may be perfectly ordinary, but we do not read the published tarball, so we cannot say what it does. View diagnostics → Partial
- 1 of 32 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency35
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- License check failed: no license is declared. See how to fix → Fail
- Actively maintained (last published 8 days ago).Pass
- Publishes a security disclosure policy (SECURITY.md).Pass
Schema Quality & AI Usability59
- AI-judged instruction clarity (good).Pass
- Context-footprint check failed: tool/resource definitions use about 1534 tokens (~191/item across 8 items; 8 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management30
- Stability observed for 9 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage71
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 0% of tool parameters carry a description.Fail
- Structured output schemas are declared (100% of tools); any adoption earns full credit.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 8 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 9 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a current MCP spec version (2026-07-28).Pass
How do I install the Ticket AI MCP server?
Ticket AI runs locally as a PyPI package, launched with uvx ticket-ai-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · ticket-ai-mcp
claude mcp add syrian963-ticket-ai-mcp -- uvx ticket-ai-mcp
{
"mcpServers": {
"syrian963-ticket-ai-mcp": {
"command": "uvx",
"args": [
"ticket-ai-mcp"
]
}
}
} {
"servers": {
"syrian963-ticket-ai-mcp": {
"command": "uvx",
"args": [
"ticket-ai-mcp"
]
}
}
} codex mcp add syrian963-ticket-ai-mcp -- uvx ticket-ai-mcp
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"syrian963-ticket-ai-mcp": {
"type": "local",
"command": [
"uvx",
"ticket-ai-mcp"
],
"enabled": true
}
}
} openclaw mcp add syrian963-ticket-ai-mcp --command uvx --arg ticket-ai-mcp
mcp_servers:
syrian963-ticket-ai-mcp:
command: "uvx"
args: ["ticket-ai-mcp"] {
"McpServers": {
"syrian963-ticket-ai-mcp": {
"Transport": "stdio",
"Command": "uvx",
"Arguments": [
"ticket-ai-mcp"
]
}
}
} assistant mcp add syrian963-ticket-ai-mcp -t stdio -c uvx -a ticket-ai-mcp
{
"mcpServers": {
"syrian963-ticket-ai-mcp": {
"command": "uvx",
"args": [
"ticket-ai-mcp"
]
}
}
} Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 20 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 27 to 30. That category is still filling its 30-day observation window: 8 days of observed history at the previous scan, 9 at this one. The score rises as the window fills, whether or not the server changes.
- 18 Sept 26 +4
- Stability: unverified → 0.23 ▲ functional
- 17 Sept 26 −15
- Malware scan: pass → unverified ▼ security
- 16 Sept 26 +15
- Malware scan: unverified → pass ▲ security
- 15 Sept 26 −15
- Malware scan: pass → unverified ▼ security
- 14 Sept 26 +15
- Malware scan: unverified → pass ▲ security
- 11 Sept 26 46
First indexed and scored.
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 20 Sept 2026 · Analysed pypi/ticket-ai-mcp@0.1.1
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | pypi |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | unreviewed | uv_build |
Background: Why install scripts are a supply-chain risk →
Dependencies 32 packages
| Packages resolved | 32 |
|---|---|
| No linked repository | 1 |
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
house_style ~59
What this team's tickets look like: template, length, labels, habits. Reads the cached profile. Says so plainly if none has been learned yet rather than returning something empty that looks like an answer.
| Name | Type | Req | Description |
|---|---|---|---|
| project | – | – | – |
| tracker | – | – | – |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
learn_conventions ~163
Learn how this team writes tickets, and cache the result. Slow: it reads the comments and linked changes of up to `sample` closed tickets, which is a few hundred API calls. Call it once per project, not once per question. Pass `from_tickets` when the user can name good examples - those are taken as given and nothing is filtered out. Leave it empty and the tracker is mined instead: closed tickets are ranked by whether a merge request shipped for them, whether anyone had to reopen them, and how many clarifying questions they drew before work started.
| Name | Type | Req | Description |
|---|---|---|---|
| from_tickets | – | – | – |
| keep | integer | – | – |
| project | – | – | – |
| sample | integer | – | – |
| tracker | – | – | – |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
review_draft ~131
Check a ticket you have written but not created yet. **Call this on your own draft before showing it to the user**, and fix what it finds rather than reporting it. It is the same measurement `review_ticket` runs, so a draft that passes here passes there. This exists so the check happens before the point of no return. Creating the ticket first notifies whoever watches the board and turns every fix into an edit with a history.
| Name | Type | Req | Description |
|---|---|---|---|
| description | string | yes | – |
| labels | – | – | – |
| project | – | – | – |
| title | string | yes | – |
| tracker | – | – | – |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
review_open_tickets ~86
Review every open ticket and list them least-aligned first. A planning tool: the answer to "what needs tidying before we can estimate any of this". One line per ticket, so call `review_ticket` for the detail on the ones that matter.
| Name | Type | Req | Description |
|---|---|---|---|
| label | – | – | – |
| limit | integer | – | – |
| project | – | – | – |
| tracker | – | – | – |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
review_ticket ~62
Measure one ticket against the learned house style. Every finding cites a count over the exemplar sample. Repeat those numbers to the user - they are the difference between this and generic advice.
| Name | Type | Req | Description |
|---|---|---|---|
| project | – | – | – |
| ticket | string | yes | – |
| tracker | – | – | – |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
template_gaps ~206
Compare the issue template this repository declares with the tickets it gets. Reads `.github/ISSUE_TEMPLATE` or `.gitlab/issue_templates` from the checkout and lines each field up against how often tickets actually carry it. The gap is the finding, and it runs both ways: - A **required field almost nobody fills in** is a form asking for something people cannot easily supply. Say so: the cheap fix is to change the form, not to nag the team. - A **section most tickets carry that no form mentions** is a convention the project grew and never wrote down. Adding it to the template is how it survives the next person who joins. Use this when asked how to improve a board rather than one ticket. It is the only tool here that reads what the project said it wanted, instead of only what it does.
| Name | Type | Req | Description |
|---|---|---|---|
| project | – | – | – |
| repo | – | – | – |
| tracker | – | – | – |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
ticket_context ~216
Gather what is known about a subject before you write the ticket for it. **Call this together with `ticket_template`, then write the ticket yourself.** This returns evidence, not prose - and specifically the evidence you cannot get by reading the repository: - which past tickets covered this ground, so you can say whether this is a duplicate before anyone spends a week on it; - **which files the merge requests for those tickets actually changed.** That exists only in the tracker's history. It is usually the fastest way to find where the work will land, and it is worth naming in the ticket. - which files in the checkout mention the subject, as a starting point for your own reading. Read the files it points at before drafting. The list is a search result, not an understanding of the code, and it will include things that merely share a word.
| Name | Type | Req | Description |
|---|---|---|---|
| project | – | – | – |
| repo | – | – | – |
| subject | string | yes | – |
| tracker | – | – | – |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
ticket_template ~71
The skeleton to fill in when writing a new ticket here. Use this before drafting, not after. It returns the sections this team actually uses and the length they actually write - then write the ticket yourself. The server has no opinion about the content.
| Name | Type | Req | Description |
|---|---|---|---|
| project | – | – | – |
| tracker | – | – | – |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
What is the Ticket AI MCP server?
Ticket AI is an MCP server listed in the public MCP registry as io.github.syrian963/ticket-ai-mcp. Measures tickets against the ones your team already shipped: sections, length, labels, duplicates. This page covers its PyPI package (ticket-ai-mcp).
Is the Ticket AI MCP server safe to use?
Ticket AI scores 51 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 20 September 2026. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the Ticket AI MCP server expose?
Ticket AI exposes 8 tools: learn_conventions, house_style, ticket_template, template_gaps, ticket_context, and 3 more. Their descriptions and schemas cost roughly 994 tokens of context every time the server is loaded.
Is the Ticket AI MCP server still maintained?
Ticket AI is still listed as active in the MCP registry. We last reached this channel on 20 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.