CURRENT•SEA
CURRENT•SEA
Объяснимые сигналы неоднозначности для текста, доступные через MCP и REST.
CURRENT•SEA — это небольшой детерминированный сервис, который помогает ИИ-агентам и программам обнаруживать формулировки, которые могут потребовать уточнения, прежде чем действовать на их основе.
Он публично доступен в качестве MCP-сервера:
MCP Registry:
io.github.Bowieboii/current-seaТекущая версия в реестре:
0.0.3Удалённая конечная точка MCP:
https://current-sea.fastapicloud.dev/mcpИнструмент MCP:
scan_ambiguity
Отправленный текст обрабатывается, но не сохраняется.
Подключение из MCP-клиента
Для MCP-клиентов, которые принимают конфигурацию mcpServers, добавьте:
{
"mcpServers": {
"current-sea": {
"type": "streamable-http",
"url": "https://current-sea.fastapicloud.dev/mcp"
}
}
}Эта конфигурация была проверена с помощью официального MCP Inspector.
Related MCP server: explain-lint
Что он обнаруживает
CURRENT•SEA в настоящее время ищет несколько распространённых форм неоднозначных формулировок, включая:
неопределённое время —
soon,later,eventuallyнеопределённое количество —
some,few,manyнеуверенные обязательства
потенциально неразрешённые ссылки —
it,them,thatсубъективные стандарты —
reasonable,effective,good,best
Каждый сигнал включает:
фразу, которая его вызвала
его расположение в тексте
категорию неоднозначности
объяснение
предлагаемый уточняющий вопрос
Пример
Ввод:
Please send it to them soon, include a few examples, and make sure it is good.Available Tools
1 toolscan_ambiguityA
Find wording that may need clarification.
Use this when an agent needs an inexpensive, deterministic, explainable check for vague timing, quantities, references, commitments, or standards. The service retains invocation metadata but never the submitted text. Signals are possible issues, not proof of ambiguity.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to inspect; it is processed but not retained. |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the behavioral disclosure burden. It does this well by stating that invocation metadata is retained but submitted text is not, and that signals are 'possible issues, not proof of ambiguity.' It also describes the tool as inexpensive, deterministic, and explainable, which helps set expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short but information-dense. The core purpose is front-loaded in the first sentence, followed by usage context and important behavioral caveats. Every sentence earns its place without repetition or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has one well-documented parameter and an output schema, so the description does not need to explain return values. It covers when to use it, what it does, and important privacy/interpretation caveats. Nothing essential for an agent to invoke it correctly appears to be missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There is only one parameter and the schema description already covers it with 'Text to inspect; it is processed but not retained.' The tool description reinforces the non-retention point but does not add new semantic meaning about the parameter beyond what the schema provides, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Find wording that may need clarification.' It clearly identifies the tool's function and even outlines the kinds of ambiguity it targets (vague timing, quantities, references, commitments, standards). With no sibling tools, this is sufficient for an agent to know exactly what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use the tool: 'Use this when an agent needs an inexpensive, deterministic, explainable check...' This gives clear context and expected conditions. It does not mention when not to use it or name alternatives, but since there are no sibling tools, this is not a significant gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v0.0.3- First observed
scan_ambiguity
TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion or overlap. The single tool's purpose is clearly defined and distinct by default.
The single tool name 'scan_ambiguity' follows a clear verb_noun convention, which is consistent and predictable. No conflicting naming styles exist.
A single tool feels too thin for most server purposes. While the scope is narrow, one tool provides minimal utility and may limit agent flexibility, aligning with the 'too few' criterion.
For the stated purpose of detecting ambiguous wording, the tool covers the core functionality well. It handles multiple ambiguity types (timing, quantities, references, commitments, standards) with no obvious gaps within that narrow domain.
Maintenance
Related MCP Connectors
Explainable ambiguity signals for text. Submitted text is processed but never retained.
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