@mgcrea/mcp-totp
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
With only one tool exposed, there is no possibility of selecting the wrong tool. The tool's purpose as a status/diagnostic endpoint is clearly distinct from any other functionality.
Naming Consistency5/5The single tool name uses consistent snake_case and clearly indicates its role as a status report. With no other tools, there is no naming pattern to conflict.
Tool Count2/5A single status tool is far below the expected scope for a TOTP server, which should include operations for key management and code generation. The description even references a totp_import_uri tool that is not exposed, making the set feel incomplete.
Completeness1/5The tool only reports capabilities and configuration state; there are no tools to actually create, import, or use TOTP secrets. This is a severe gap for a server named mcp-totp.
Average 4.9/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 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
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already state readOnlyHint, idempotentHint, and destructiveHint false, and the description adds behavior beyond those: it explains the server cannot read Passwords.app because no interface exists, and that the server mints codes locally from its own seed copy. This gives the agent critical context about system limitations without contradicting the annotations.
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 front-loaded with the core purpose in the first sentence, then moves to usage guidance and a clarifying limitation. The length is justified because it conveys essential integration context about Passwords.app that would otherwise be invisible to an agent. Every sentence earns its place and no filler is present.
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?
With no output schema, the description enumerates exactly what the agent will learn from the call: reachability of the seed store, registered labels and their sources, and write-enable status. It also covers when to call and what the tool cannot do, making it fully self-contained for an agent to invoke and interpret correctly.
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 schema is empty, so there is nothing for the description to add about parameters. The baseline of 4 for a no-parameter tool applies, and the description does not need to compensate for any schema gaps since none exist.
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 opens with a specific verb and resource: it 'reports' the server's capabilities, listing concrete output items such as seed store reachability, registered labels and their origins, and write-enable state. This is unambiguous and cannot be confused with any other operation, especially since there are no sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly instructs when to call: 'Call this first when a tool you expected is missing — an absent tool means missing configuration rather than a bug.' It also clarifies what the server is NOT and points to totp_import_uri as the relevant alternative for syncing seeds, giving the agent both a trigger condition and a related tool reference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
GitHub Badge
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- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
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