| name | translate-incoming |
| description | Decode subtext and ambiguity in an incoming corporate message via the NeuroDock translation server. Returns explicit ask, ranked subtext, ambiguity spans anchored verbatim, and a recommended next action. |
translate-incoming
Wrapper for translate_incoming on the local NeuroDock translation server
(neurodock-translation). Takes a verbatim incoming message and returns a
structured decode useful for autistic / AuDHD readers who want the implicit
corporate-speak made explicit.
Design rationale: ADR 0005 — translation tool design
— ambiguity spans MUST anchor verbatim to the input.
When to use
- The user pastes a Slack / email / Linear / GitHub message and says
"what does this actually mean" or "decode this".
- Before drafting a reply, when the user is unsure what is actually being
asked.
What it does
Calls mcp__neurodock-translation__translate_incoming. The server
itself performs no LLM calls — it orchestrates a prompt template against
the caller's MCP-client LLM (local Ollama or cloud, per the user's
profile) and returns the typed response.
The response contains:
explicit_ask — the surface-level request, paraphrased plainly. Null
if the message is a status update with no ask.likely_subtext[] — up to 5 ranked hypotheses about implicit meaning,
each with a 0..1 confidence score.ambiguity — a report of spans whose meaning is unclear, with
{start_char, end_char, reason, note}. The quoted span MUST equal
text[start_char:end_char] exactly. This is the verbatim-anchor
invariant from ADR 0005.recommended_next_action — a single coarse action + a short
justification.model_provenance — which model ran the call (local vs cloud, model id).
How to invoke
Input shape:
{
"text": "Hey — can we revisit the rollout timeline? I'm not sure everyone is aligned.",
"channel": "slack",
"thread_context": [
"We agreed last sprint to ship the rollout by end of May.",
"Two engineers flagged risk on the migration script."
]
}
Field rules:
text (required): 1–8000 chars. Stored only in-memory; never logged.channel (optional): one of the v0.1.0 enum
(slack, email, teams, linear, github, generic). Influences
register expectations. Use generic if unsure.thread_context (optional): up to 20 prior messages, oldest first.
Each entry is a verbatim quote, 1–2000 chars.target_language (optional): BCP-47 tag (e.g. en-IE, de, ja).
Falls back to English prompts if no language pack is registered.
The verbatim-anchor invariant
Every ambiguity span the caller receives satisfies:
input.text[span.start_char : span.end_char] == (the substring shown in note context)
The server's VERBATIM_ANCHOR_FAILED error guarantees this — the LLM
cannot fabricate a span. If the response is invalid, the server rejects
and the caller retries; it does not "fix up" mismatches.
When rendering ambiguity to a user, always quote the verbatim substring,
not a paraphrase. The whole point of the schema's anchor is that the
user can trust the quote.
Errors
TEXT_REQUIRED — empty text.TEXT_TOO_LONG — over 8000 chars. Split or excerpt.CHANNEL_UNKNOWN — channel not in the v0.1.0 enum. Use generic.LANGUAGE_PACK_MISSING — target_language has no pack. Retry without
it to fall back to English.MODEL_UNAVAILABLE — caller's MCP client could not reach its LLM.
Server does not retry; caller decides to surface or queue.VERBATIM_ANCHOR_FAILED — LLM fabricated a span. Retry the call.
Limitations
- The translation quality depends on the caller's configured model.
Local 7B Ollama is more likely to hallucinate than a frontier cloud
model, and the verbatim-anchor enforcement will reject those responses.
- The server does not store transcripts. If the user wants to come back
to a decode later, the caller must persist it.
Voice
When surfacing subtext to the user, lead with the top-confidence
hypothesis and label it as a hypothesis ("most likely they mean X"), not
as a fact. Quote ambiguous spans verbatim. Do not perform the recommended
next action without confirmation — the user reads the brief; the user
decides.
