flutter-stripe-mcp
Server Quality Checklist
Latest release: v0.2.0
- Disambiguation5/5
Only one tool exists (diagnose_setup), so there is no possibility of confusion with other tools. The tool's purpose is clearly described and unique.
Naming Consistency5/5With a single tool, naming is trivially consistent. The verb_noun pattern 'diagnose_setup' follows a clear and predictable style.
Tool Count3/5One tool is acceptable for a narrow diagnostic purpose, but it feels thin for a server branded as 'flutter-stripe-mcp'. Typically, such servers would have 3-15 tools to cover both setup and operations.
Completeness2/5The tool only diagnoses setup issues. There are no tools for actual Stripe integration, configuration, or operations, creating significant gaps for a server targeting Flutter + Stripe workflows.
Average 4.8/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 9 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 indicate readOnlyHint, openWorldHint, and idempotentHint. The description adds valuable behavioral details: returns a 'note' if the comment thread was too long, and returns an error object with a 'kind' field on failure. This goes beyond the structured 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 well-structured with a clear summary, usage guidance, parameter description, and return format. It is concise, front-loaded, and every sentence adds value without redundancy.
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 the tool's simplicity (1 parameter, output schema provided), the description covers all necessary aspects: input, output, error handling, and usage context. No missing information for effective agent decision-making.
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 description coverage is 0%, but the description adds crucial meaning: 'issue_number: The GitHub issue number (e.g. 1234), not a URL.' This clarifies the format and avoids confusion. For a single parameter, this is effective.
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?
Description clearly states 'Fetch the full body and comments of one flutter_stripe GitHub issue.' This is a specific verb+resource pair, and it distinguishes from its sibling search_flutter_stripe_issues by focusing on retrieving full details of a single issue.
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?
Explicitly tells when to use this tool: 'after search_flutter_stripe_issues has identified a candidate issue number.' It also explains why: 'to read the complete discussion — maintainer replies and community comments often contain the actual fix, workaround, or root cause.' This provides clear context and an alternative tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true and idempotentHint=true. The description adds specific behavioral details: lists exact checks (Kotlin version, Gradle, etc.) and return structure. No contradictions, and it enriches agent understanding of 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?
The description is well-organized with clear sections (Android checks, iOS checks, Args, Returns). It is concise yet comprehensive, using bullet-like structure and plain text efficiently.
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 the tool's multi-platform checks, the description covers all relevant aspects: project path requirement, platform-specific checks, and return format. It provides sufficient detail for an agent to understand the tool's full scope.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description explains the 'project_path' parameter comprehensively: 'Absolute path to the Flutter project root (the directory containing android/ and ios/).' This adds significant meaning beyond the schema's minimal 'Project Path' title, which had 0% coverage.
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: diagnose Flutter + Stripe project setup. It lists specific checks for Android and iOS, making the scope precise. With no sibling tools, it stands alone effectively.
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 implies usage for diagnosing Flutter+Stripe projects. It does not explicitly state when not to use it or alternatives, but given no siblings, the absence is acceptable. It provides clear context for use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses key behaviors beyond annotations: automatic retry with broadened search on no exact match, marking results with 'broadened_search', and return format with error handling. This adds value beyond the readOnlyHint, openWorldHint, and idempotentHint 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 well-structured with clear paragraphs for purpose, usage, behavior, and parameters. It is concise without redundancy, front-loading the main goal and effectively using space for each aspect.
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 the tool has 3 parameters (all fully explained), an output schema described in the return format, and error handling notes, the description is complete. It provides all necessary context for correct invocation and interpretation of results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully explains all three parameters: query (free-text, example given), state (enum with default), and limit (range and default). It also notes the output includes error on failure, compensating for missing schema descriptions.
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 searches GitHub issues in a specific repository (flutter-stripe/flutter_stripe) and distinguishes it from the sibling tool get_flutter_stripe_issue by mentioning that it returns excerpts for relevance judgment and advises using the sibling for full details.
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?
The description explicitly tells when to use (to check if a problem is already reported) and provides context such as automated broadened search on no exact match. It also mentions the alternative tool for full discussion, giving clear usage guidance.
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 the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
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