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Latinum-Formal-Methods

Weather MCP Tool

by Latinum-Formal-Methods
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Python
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🌤️ Weather MCP Tool (Latinum Paywalled Agent Tool)

A minimal Model Context Protocol (MCP) tool that provides real-time weather information for any city. Access to forecasts is paywalled via the Latinum Wallet MCP server, requiring Solana Devnet payment — except for Dublin, which is available for free.

🔧 Developper testing

To install your local build as a CLI for testing with Claude:

git clone https://github.com/Latinum-Agentic-Commerce/latinum-wallet-mcp.git
cd weather_mcp
python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip setuptools wheel
pip install --upgrade --upgrade-strategy eager -r requirements.txt
pip install --editable .

Edit your Claude Desktop config:

~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
      "weather_mcp": {
          "command": "/Users/{YOUR_USER}/workspace/weather_mcp/.venv/bin/python",
          "args": [
              "/Users/{YOUR_USER}/workspace/weather_mcp/weather_mcp/server_stdio.py"
          ]
      }
  }
}

Then restart Claude.

📑 PyPI Publishing

python3 -m venv .venv && source .venv/bin/activate
pip install --upgrade pip setuptools wheel
pip install -r requirements.txt
rm -rf dist/ build/ *.egg-info
python3 -m build
python3 -m twine upload dist/*
pipx upgrade weather-mcp

See the output in a path like: https://pypi.org/project/weather-mcp/

💳 How It Works

  • ❓ Ask: What's the weather in Paris? → Claude responds instantly.

  • ❌ Ask: What's the weather in London? → Claude gets a 402 and triggers the wallet.

  • ✅ Claude pays using Latinum Wallet and retries.

Available Tools

1 tool
get_weatherD

Call self as a function.

ParametersJSON Schema
NameRequiredDescriptionDefault
cityYes
signed_b64_payloadNo

TDQS

D1.1/5.0
Behavior1/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. However, it reveals nothing about the tool's behavior—whether it's a read-only operation, requires authentication (implied by 'signed_b64_payload'), has rate limits, or what the output might be. This leaves critical operational traits completely opaque.

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

Conciseness2/5

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

The description is extremely brief ('Call self as a function.'), which might seem concise, but it's under-specified rather than efficient. It wastes its single sentence on a tautology that provides no value, failing to front-load useful information or structure content meaningfully for the agent.

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

Completeness1/5

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

Given the tool's complexity (2 parameters, no output schema, no annotations), the description is completely inadequate. It lacks purpose, usage, behavioral details, parameter explanations, and any mention of return values, making it insufficient for an agent to understand or invoke the tool correctly in any context.

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

Parameters1/5

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

The input schema has 2 parameters with 0% description coverage, meaning no parameter details are documented in the schema. The description adds no semantic information about parameters like 'city' (e.g., format, examples) or 'signed_b64_payload' (e.g., purpose, when required), failing to compensate for the schema's lack of documentation.

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

Purpose1/5

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

The description 'Call self as a function' is a tautology that merely restates the tool's name 'get_weather' in abstract terms, providing no information about what the tool actually does. It fails to specify the verb (e.g., retrieve, fetch) or resource (e.g., weather data, forecasts), making it completely unhelpful for understanding the tool's purpose.

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

Usage Guidelines1/5

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

The description offers no guidance on when to use this tool, such as for checking current weather, forecasts, or specific conditions. With no sibling tools mentioned, there are no alternatives to differentiate from, but the description still provides zero context or prerequisites for usage, leaving the agent with no actionable information.

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. Dates show when Glama detected each change.

  1. 1 tool updatev0.0.1
    • First observedget_weather
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TDQS

D1.8/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools, as there are no other tools to confuse it with. The single tool's purpose is clearly defined as retrieving weather information.

Naming Consistency5/5

Since there is only one tool, it inherently exhibits perfect naming consistency with itself. The tool name 'get_weather' follows a clear verb_noun pattern, which would be consistent if more tools existed.

Tool Count2/5

A single tool for a weather server is too few for the apparent scope, as weather-related operations typically involve more functionality (e.g., forecasts, alerts, historical data). This minimal set feels thin and incomplete for the domain.

Completeness1/5

The tool surface is severely incomplete for a weather domain, offering only basic current weather retrieval. There are significant gaps, such as lack of forecast data, location-based searches, or historical weather information, which will likely cause agent failures in broader tasks.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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