MCP Weather Server
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
With only a single tool, there is no possibility of confusion or overlap. The tool's purpose is clear and distinct.
Naming Consistency5/5The tool name 'get_hourly_weather' follows a clear verb_noun pattern, which is consistent even though it is the only tool. Naming conventions are not violated.
Tool Count2/5A single tool is too few for a server that claims to be a weather server. The apparent scope is broader than just hourly forecasts, so the tool count feels inadequate.
Completeness2/5The tool surface only covers hourly forecasts, missing standard weather operations like current conditions, daily forecasts, or alerts. This leaves significant gaps for agents expecting comprehensive weather data.
Average 3.8/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under The Unlicense.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add
glama.jsonto the root of your repository:{ "$schema": "https://glama.ai/mcp/schemas/server.json", "maintainers": [ "your-github-username" ] }Then . Browse examples.
Add related servers to improve discoverability.
How to sync the server with GitHub?
Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.
To manually sync the server, click the "Sync Server" button in the MCP server admin interface.
How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden for behavioral disclosure. It only states 'Get hourly weather forecast' but does not describe the response format, potential limitations (e.g., number of hours returned, timezone handling), or any side effects. The description is essentially a restatement of the tool's name with no added behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: a one-sentence purpose followed by clearly labeled arguments. Every sentence is essential, and the Args section is formatted for easy parsing. No filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema and no annotations, so the description should explain what the tool returns to be fully complete. It only states 'hourly weather forecast' without detailing the response fields (e.g., temperature, precipitation, wind). For an AI agent, this lack of return-value information leaves a significant gap in understanding the tool's full output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides only parameter names and types, with no descriptions (0% coverage). The description compensates by explaining 'location' as a city or location name with examples, and 'unit' with its default and allowed values ('C' for Celsius, 'F' for Fahrenheit). This adds meaningful semantics beyond the schema, though it could be more comprehensive (e.g., supporting zip codes or other formats).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Get hourly weather forecast for a location.' This is a specific verb (get) and resource (hourly weather forecast), and it is distinct from any potential siblings. No ambiguity in purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly state when to use this tool versus alternatives, but with no sibling tools listed, the context is clear. It provides the required input (location) and optional unit, implying the tool is for any location's hourly forecast. It lacks explicit exclusions or alternative recommendations, but this is acceptable given no alternatives exist.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
GitHub Badge
Glama performs regular codebase and documentation scans to:
- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
Our badge communicates server capabilities, safety, and installation instructions.
Card Badge
Copy to your README.md:
Score Badge
Copy to your README.md:
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/charley-forey/weather-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server