Skip to main content
Glama
deenesjakoruzh
Bowieboii

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
textYesText to inspect; it is processed but not retained.

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A4.3/5.0
Behavior4/5

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.

Conciseness5/5

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.

Completeness5/5

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.

Parameters3/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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. 1 tool updatev0.0.3
    • First observedscan_ambiguity
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

ActivityMaintained
ResponsivenessNo issues

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
  • decompose by echology-io

    decomposeofficial

    echology-io
    A
    license
    Not graded
    quality
    C
    maintenance
    Classifies text into structured semantic units with authority, risk, and attention scores. Enables deterministic preprocessing for AI agents to filter and route content without using an LLM.
    10
    MIT