frisk-mcp
Server Quality Checklist
Latest release: v0.0.1
- Disambiguation5/5
With only one tool, there is no possibility of confusion or overlap. The tool's purpose is singular and clearly defined.
Naming Consistency5/5The single tool uses a clear verb_noun pattern ('screen_payment'), which is consistent and predictable.
Tool Count3/5One tool feels thin for a general-purpose server, but for a narrowly focused payment screening service, it may be sufficient. It's borderline on the calibration scale.
Completeness4/5The tool covers the essential screening workflow (address checks, policy, etc.), but lacks auxiliary capabilities like configuration or reporting. Minor gaps that agents can work around.
Average 4.7/5 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
- 16 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.
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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
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that it runs offline, returns a recommendation with reasons, leaves the final decision to the caller, and notes the optional FRISK_API_KEY for hosted signals. This goes beyond the annotations and fully describes behavior.
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 concise sentences that convey purpose, behavior, and optional configuration without any redundant or tangential information.
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?
Given no output schema, the description explicitly names the return categories (allow/review/block with reasons) and clarifies that it is advisory and offline, covering all essential context an agent needs.
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?
Schema covers all 8 parameters with clear descriptions. The description adds relational context (e.g., dynamic-payTo comparison between observedPayTo and counterparty, spending policy tied to allowedAssets/maxPerCall/strictness), enhancing the schema's meaning.
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?
Clearly states the action (screen), the object (counterparty about to be paid), and the outcome (allow/review/block with reasons). No ambiguity about the tool's 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?
Implies when to use (before paying) and provides operational context (runs entirely offline, advisory only). Does not explicitly contrast with alternatives, but no sibling tools are present.
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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