agentmail
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of confusion between tools. The single tool has a clear, specific purpose.
Naming Consistency5/5Although there is only one tool, its name 'risk_score' is descriptive and follows a reasonable noun_noun pattern. No inconsistencies exist.
Tool Count3/5A single tool is borderline for a server. However, the tool is focused on a critical, well-defined action (fraud scoring before payment), so it is not overly thin.
Completeness2/5The server only provides scoring before payment, but agents may need to retrieve past screening results or update risk profiles, which are missing. This limits the server's utility for complex workflows.
Average 4.4/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 1 of 1 community issues answered or closed in the last 6 months
- 243 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.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
This server has been verified by its author.
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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?
No annotations are provided, so the description carries full burden. It discloses that the tool combines counterparty signals, amount anomalies, sanctions screen, and heuristics, and returns a recommendation (allow/review/decline) with the meaning of 'decline' (abort payment). It also describes the output structure. No contradictions.
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 (6 sentences) and front-loaded with the primary purpose. It uses a clear, structured format with bullet points for recommendations and valid values. No extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 5 parameters and an output schema, the description provides sufficient behavioral overview and output details. It lacks parameter-level descriptions for all fields, but the output schema compensates. Overall, it is complete for the complexity level.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does 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 adds context for 'rail' and 'category' by listing valid values, and explains the output structure. However, it does not describe 'counterparty_id', 'amount', or 'currency' beyond their roles in the overall process. The added value is moderate.
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 purpose: 'Score a transaction's fraud risk BEFORE authorizing payment.' It uses a specific verb ('score') and resource ('risk'), and provides context on when to use it ('right before an agent pays'). No sibling tools exist, so differentiation is unnecessary.
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 'Call right before an agent pays,' providing clear timing. It also lists valid values for 'rail' and 'category', helping the agent use correct inputs. It does not include explicit when-not-to-use statements, but the guidance is strong.
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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