mcp-avangenio-services
Server Quality Checklist
Latest release: v0.1.1
- Disambiguation5/5
Only one tool exists, so there is no possibility of confusing it with another tool. The single tool has a clear, specific purpose.
Naming Consistency4/5The tool name follows a clear verb_noun pattern ('avangenio_get_status'), but with only one tool there is no broader pattern to evaluate. Still, the name is descriptive and consistent with common MCP conventions.
Tool Count4/5A single tool is minimal but fully appropriate for the narrow scope of retrieving service status. It feels slightly thin compared to richer servers, but the count matches the stated purpose without being wasteful.
Completeness5/5The tool covers all aspects of the described purpose: internet status, bandwidth, battery, and electrical state. Returns both normalized and raw values, so no obvious gaps in the domain.
Average 4.3/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 4 commits in the last 12 weeks
- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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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
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint, so the description only needs to add extra context. It does that by revealing it reads from an external URL and returns both normalized and raw data. This adds non-obvious behavioral details beyond the annotations, though it doesn't describe potential errors or rate limits.
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?
Two sentences, front-loaded with a specific verb and resource, and no filler. Every sentence provides meaningful information about input, output, and data source.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameterless read-only tool with no output schema, the description covers the essential context: what it reads, from where, and what the return structure looks like. It is complete enough for an agent to invoke it and understand the result.
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 tool has zero parameters and the description explicitly states 'Takes no arguments,' which is consistent with the empty schema. Since there are no parameters to clarify, the baseline 4 applies.
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 'Gets the current status of Avangenio's services' and enumerates the specific service types (Internet, bandwidth, battery level, electrical service). It also names the data source URL. With no siblings, it fully disambiguates the tool's function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving service status but does not explicitly state when to use it vs alternatives. Since there are no sibling tools, this is less critical, but the absence of explicit 'when-to-use' direction keeps it at a minimum viable level.
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.
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