Skip to main content
Glama
Sign In
deenesjakoruzh
CURRENT•SEA

CURRENT•SEA

by Bowieboii
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

CURRENT•SEA

Explainable ambiguity signals for text, available over MCP and REST.

CURRENT•SEA is a small, deterministic service that helps AI agents and software detect wording that may need clarification before acting on it.

It is publicly available as an MCP server:

  • MCP Registry: io.github.Bowieboii/current-sea

  • Current Registry version: 0.0.3

  • Remote MCP endpoint: https://current-sea.fastapicloud.dev/mcp

  • MCP tool: scan_ambiguity

Submitted text is processed but not retained.

Connect from an MCP client

For MCP clients that accept an mcpServers configuration, add:

{
  "mcpServers": {
    "current-sea": {
      "type": "streamable-http",
      "url": "https://current-sea.fastapicloud.dev/mcp"
    }
  }
}

This configuration was verified with the official MCP Inspector.

Related MCP server: explain-lint

What it detects

CURRENT•SEA currently looks for several common forms of ambiguous wording, including:

  • vague timing — soon, later, eventually

  • vague quantities — some, few, many

  • uncertain commitments

  • potentially unresolved references — it, them, that

  • subjective standards — reasonable, effective, good, best

Each signal includes:

  • the phrase that triggered it

  • its location in the text

  • the ambiguity category

  • an explanation

  • a suggested clarification question

Example

Input:

Please send it to them soon, include a few examples, and make sure it is good.

Available Tools

1 tool
scan_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.

Inputs: text (required). Returns: object

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A4.3/5.0

    Scored across 1 tool

    Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap. The single tool's purpose is clearly defined and distinct by default.

    Naming Consistency5/5

    The single tool name 'scan_ambiguity' follows a clear verb_noun convention, which is consistent and predictable. No conflicting naming styles exist.

    Tool Count2/5

    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.

    Completeness5/5

    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

    ActivityNo data
    ResponsivenessOutdated data

    Resources

    Related MCP Connectors

    Related MCP Servers

    • MCP Prompt Optimizer

      grandinh
      A
      license
      B
      quality
      D
      maintenance
      Automatically analyzes and optimizes AI prompts by calculating clarity scores, detecting risks, asking clarifying questions, and adding domain-specific requirements to improve AI interaction quality.
      1
      MIT

    • jev-mcp

      jkudish
      A
      license
      A
      quality
      A
      maintenance
      Enables agents to verify claims against cited evidence, screen content for prompt injection and relevance before reading it, and rank candidates by meaning, all with calibrated probability verdicts.
      12
      5,273 npm
      574
      MIT

    • Prompt Optimizer MCP

      terngg
      A
      license
      A
      quality
      C
      maintenance
      Enables AI agents to turn rough or ambiguous requests into clearer, context-aware execution guidance through optimization, inspection, adaptation, evaluation, comparison, context compression, and instruction-building tools while preserving original intent and staying local-first.
      9
      27 npm
      MIT