NeuroDock Translation
PYPI · NEURODOCK-MCP-TRANSLATION · SCANNED SEP 20
Decode subtext, tone, and ambiguity in messages; rewrite outgoing replies; brief meetings.
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 Security50
- Malware scan not yet available for this package.Unverified
- No known CVEs affecting this package version or its production dependencies.Pass
- Runs hatchling.build at install time, a recognised native-build step with no shell scripting around it. View diagnostics → Pass
- 1 of 16 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency48
- 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
- Clear OSI-approved license (AGPL-3.0-or-later).Pass
- Actively maintained (last published 93 days ago).Pass
- Publishes a security disclosure policy (SECURITY.md).Pass
Schema Quality & AI Usability51
- AI-judged instruction clarity (fair).Partial
- Tool/resource definitions use about 352 tokens (~88/item across 4 items; 4 tools + 0 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management80
- Stability observed for 24 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 4 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 4 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 NeuroDock Translation MCP server?
NeuroDock Translation runs locally as a PyPI package, launched with uvx neurodock-mcp-translation. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · neurodock-mcp-translation
claude mcp add tlennon-ie-neurodock-mcp-translation -- uvx neurodock-mcp-translation
{
"mcpServers": {
"tlennon-ie-neurodock-mcp-translation": {
"command": "uvx",
"args": [
"neurodock-mcp-translation"
]
}
}
} {
"servers": {
"tlennon-ie-neurodock-mcp-translation": {
"command": "uvx",
"args": [
"neurodock-mcp-translation"
]
}
}
} codex mcp add tlennon-ie-neurodock-mcp-translation -- uvx neurodock-mcp-translation
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"tlennon-ie-neurodock-mcp-translation": {
"type": "local",
"command": [
"uvx",
"neurodock-mcp-translation"
],
"enabled": true
}
}
} openclaw mcp add tlennon-ie-neurodock-mcp-translation --command uvx --arg neurodock-mcp-translation
mcp_servers:
tlennon-ie-neurodock-mcp-translation:
command: "uvx"
args: ["neurodock-mcp-translation"] {
"McpServers": {
"tlennon-ie-neurodock-mcp-translation": {
"Transport": "stdio",
"Command": "uvx",
"Arguments": [
"neurodock-mcp-translation"
]
}
}
} assistant mcp add tlennon-ie-neurodock-mcp-translation -t stdio -c uvx -a neurodock-mcp-translation
{
"mcpServers": {
"tlennon-ie-neurodock-mcp-translation": {
"command": "uvx",
"args": [
"neurodock-mcp-translation"
]
}
}
} 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 −17
- Malware scan: pass → unverified ▼ security
- Stability: pass → 0.80 functional
- 19 Sept 26 +15
- Malware scan: unverified → pass ▲ security
- Stability: 0.97 → pass security
- 18 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 93 to 97. That category is still filling its 30-day observation window: 28 days of observed history at the previous scan, 29 at this one. The score rises as the window fills, whether or not the server changes.
- 17 Sept 26 −15
- Malware scan: pass → unverified ▼ security
- 15 Sept 26 +16
- Malware scan: unverified → pass ▲ security
- 14 Sept 26 −15
- Malware scan: pass → unverified ▼ security
- 13 Sept 26 −2
- Stability: pass → 0.80 functional
- 12 Sept 26 0
- Stability: 0.97 → pass security
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/neurodock-mcp-translation@0.3.0
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 | allowlisted | hatchling.build |
Background: Why install scripts are a supply-chain risk →
Dependencies 16 packages
| Packages resolved | 16 |
|---|---|
| Stale | 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 →
brief_meeting Brief meeting ~86
Convert a meeting transcript into a four-section structured brief: my_asks, others_asks, decisions, ambiguous_items. Every ambiguous_item is anchored to a verbatim transcript span; the server rejects responses where the anchor cannot be located.
| Name | Type | Req | Description |
|---|---|---|---|
| me | string | yes | – |
| project | – | – | – |
| reader_context | – | – | – |
| speakers | – | – | – |
| transcript | string | yes | – |
Structured output declared, but exposes no named fields.
No examples provided.
check_tone Check tone ~94
Score an outgoing message on directness / warmth / urgency axes (0..100), optionally relative to a baseline of the sender's prior messages, and flag phrases that deviate substantially from baseline or from a target register. Returns deterministic axes plus an LLM-refinement prompt.
| Name | Type | Req | Description |
|---|---|---|---|
| baseline_messages | – | – | – |
| channel | – | – | – |
| reader_context | – | – | – |
| target_register | – | – | – |
| text | string | yes | – |
Structured output declared, but exposes no named fields.
No examples provided.
rewrite_outgoing Rewrite outgoing ~91
Rewrite an outgoing message toward a target register while preserving caller-named technical terms. The deterministic baseline applies register-specific surface transforms; the LLM refinement prompt produces a stronger rewrite while keeping the same preservation contract.
| Name | Type | Req | Description |
|---|---|---|---|
| channel | – | – | – |
| preserve_intent | boolean | – | – |
| preserve_terms | – | – | – |
| reader_context | – | – | – |
| target_register | string | yes | – |
| text | string | yes | – |
Structured output declared, but exposes no named fields.
No examples provided.
translate_incoming Translate incoming message ~81
Decode subtext, ambiguity, and the likely implicit ask in an incoming message. Returns a deterministic baseline analysis plus a structured prompt the caller's MCP client MAY execute to refine the baseline against its own LLM.
| Name | Type | Req | Description |
|---|---|---|---|
| channel | – | – | – |
| reader_context | – | – | – |
| target_language | – | – | – |
| text | string | yes | – |
| thread_context | – | – | – |
Structured output declared, but exposes no named fields.
No examples provided.
What is the NeuroDock Translation MCP server?
NeuroDock Translation is an MCP server listed in the public MCP registry as io.github.tlennon-ie/neurodock-mcp-translation. Decode subtext, tone, and ambiguity in messages; rewrite outgoing replies; brief meetings. This page covers its PyPI package (neurodock-mcp-translation).
Is the NeuroDock Translation MCP server safe to use?
NeuroDock Translation scores 61 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 NeuroDock Translation MCP server expose?
NeuroDock Translation exposes 4 tools: translate_incoming, check_tone, rewrite_outgoing, brief_meeting. Their descriptions and schemas cost roughly 352 tokens of context every time the server is loaded.
Is the NeuroDock Translation MCP server still maintained?
NeuroDock Translation 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.
What licence is the NeuroDock Translation MCP server under?
NeuroDock Translation declares the AGPL-3.0-or-later licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.