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omega-stenographer-mcp

by VrtxOmega
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Python
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stenographer_ingest_exchange

Ingest conversation turns to extract decisions and blockers, detect failure signatures, and compress stale context into searchable briefs for cross-system correlation.

Instructions

Ingest a conversation turn. Call after every significant user or assistant message. Automatically: extracts decisions/blockers via regex, runs NAFE failure-signature scan (heuristic flags do not establish truth), checks for cross-system CLAEG TERMINAL_SHUTDOWN events, and compresses every STENO_TURN_LIMIT turns into session-scoped tiered briefs. Pass trace_id from omega_preload_context to enable cross-system correlation across Omega Brain, SSWP, and Stenographer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
roleYes
contentYes
trace_idNoVERITAS trace ID (VT-YYYYMMDD-xxxxxxxx) from omega_preload_context for cross-system correlation.
session_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv2.0.0
    • addedInput schema / properties / session_id / minLength
      Added value: +1
    • addedInput schema / properties / trace_id
      Added value: +{
+  "description": "VERITAS trace ID (VT-YYYYMMDD-xxxxxxxx) from omega_preload_context for cross-system correlation.",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "role",
-  "content"
-]New value: +[
+  "role",
+  "content",
+  "session_id"
+]
  2. First observed

TDQS

A3.6/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden and discloses significant automatic behavior: regex extraction, NAFE scan, CLAEG TERMINAL_SHUTDOWN checks, and periodic compression into briefs. It also cautions that heuristic flags do not establish truth. Side effects are partially described but not exhaustively.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but not wordy; it packs a lot of behavior into a single sentence. Jargon like NAFE, CLAEG, and STENO_TURN_LIMIT is unexplained, but the structure is efficient and free of filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

It explains when to call and what automatic processes occur, but it does not describe what the tool returns or the outcome of the compression, and key constants are undefined. With no output schema, this leaves some gaps for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is low (only trace_id has a description). The description adds useful context for trace_id ('from omega_preload_context to enable cross-system correlation'), but role, content, and session_id rely on their obvious names and types. It partially compensates but does not fully cover the gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool ingests a conversation turn and provides the trigger ('after every significant user or assistant message'). It is distinguishable from sibling tools like query_history and get_brief by the ingest action, though it does not explicitly name alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives a clear call condition ('after every significant user or assistant message') but does not explicitly state when not to use it or compare it with sibling tools. The usage context is implied rather than fully specified.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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