job-mcp
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or misselection. The tool's purpose is clearly distinct simply because it is the only tool.
Naming Consistency5/5A single tool name cannot be inconsistent with itself. 'send_email' follows a clear verb_noun convention, and there are no other names to compare against.
Tool Count2/5One tool feels too thin for a server named 'job-mcp', which implies a broader job-application workflow. While sending an email is a concrete task, the server likely needs supporting tools for content generation or attachment handling to be useful.
Completeness2/5The surface is severely limited: it only sends an email. Missing operations include email preview, attachment support, and any integration with job-application data, leaving obvious gaps for real-world workflows.
Average 4/5 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 status not available
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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?
Since no annotations are provided, the description carries the behavioral disclosure burden. It transparently states that the tool sends an email via SMTP, which is an external side effect, and adds a practical requirement to provide polished HTML. It omits details like SMTP prerequisites or failure modes, but the core behavior is explicit.
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 two sentences long, front-loaded with the core action, and every clause adds value: the action, the use case, and the body-format expectation. No redundant or generic phrases.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 7 parameters, no annotations, and no output schema, so the description must be reasonably self-contained. It covers the main action, the intended purpose, and the key body requirement. However, it leaves several parameter semantics and any error/response behavior unaddressed. The operation is simple and the names are fairly self-evident, so the gaps are moderate rather than severe.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It only explains body_html and optionally body_text; to, subject, cc, bcc, and reply_to are left undocumented. While the parameter names are conventional, the description does not add sufficient meaning for a zero-coverage schema, especially for required fields like 'to' and 'subject'.
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 action ('Send an email via SMTP using Red Mail') and the specific use case ('final, tailored job-application email to the recruiter/HR address'). It is a precise verb+resource combination, with no ambiguity or tautology.
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 explicitly says 'Use this to send the final, tailored job-application email to the recruiter/HR address', giving a clear when-to-use context. There are no sibling tools listed, so there is no opportunity to name alternatives or when-not scenarios, but the intended scenario is well defined.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
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