Flag MCP
Server Quality Checklist
- Disambiguation5/5
With only one tool available, there is no ambiguity or risk of tool misselection. The single tool has a clear, distinct purpose that does not overlap with any other tools.
Naming Consistency5/5The single tool follows a clear snake_case naming convention. While 'interactive' is an adjective rather than a verb, there are no other tools to create inconsistency, so the naming pattern is uniform.
Tool Count2/5A single tool is insufficient for the apparent 'Flag' domain suggested by the server name. Even for a minimal utility server, one generic feedback tool represents a severely undersized tool surface that likely cannot fulfill the server's intended purpose.
Completeness1/5The server named 'Flag MCP' implies a feature flag or flagging domain, yet provides only a generic user feedback tool with no flag creation, management, querying, or lifecycle operations. This represents a severely incomplete surface for the stated purpose.
Average 3.9/5 across 1 of 1 tools scored.
See the tool scores section below for per-tool breakdowns.
This repository includes a README.md file.
This repository includes a LICENSE file.
Latest release: v0.1.1
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.
Add a glama.json file to provide metadata about your server.
- This server provides 1 tool. View schema
No known security issues or vulnerabilities reported.
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Tool Scores
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the interaction pattern (blocking request for user input, option-based responses) but lacks technical behavioral traits such as timeout behavior, session persistence, or data handling. It covers 'how to use' but omits 'what happens under the hood' details expected for a user-input tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description uses clear structural headers (Usage method, Usage tips, When to use) but is verbose due to extensive bilingual content. The Chinese examples, while helpful for behavior modeling, make the description longer than necessary for agent consumption, and the language mixing reduces structural coherence.
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 presence of an output schema, the description appropriately focuses on behavioral guidance rather than return values. It comprehensively covers invocation patterns and option structuring, though it would benefit from mentioning timeout handling or error conditions for full completeness.
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 coverage is 100%, establishing a baseline of 3. The description adds semantic value beyond the schema by specifying in the Chinese text that predefined_options should include a recommended choice with rationale and an 'end' option, providing substantive usage guidance for parameter population that the raw schema lacks.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The opening sentence clearly states the tool requests interactive feedback and supports text/image attachments. However, the extensive Chinese instructions, while useful, create a bilingual structure that slightly reduces immediate clarity for agents parsing primarily English content.
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 Chinese text under '什么时候使用' (when to use) provides explicit when/when-not guidance, specifically stating to invoke this tool when work is completed instead of directly ending the process, and when encountering decision points requiring user confirmation. It also explicitly references '使用方式' (usage method) and '使用技巧' (usage tips) for detailed alternatives.
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.
Our badge communicates server capabilities, safety, and installation instructions.
Card Badge
Copy to your README.md:
Score Badge
Copy to your README.md:
How to claim the server?
If you are the author of the server, you simply need to authenticate using GitHub.
However, if the MCP server belongs to an organization, you need to first add glama.json to the root of your repository.
{
"$schema": "https://glama.ai/mcp/schemas/server.json",
"maintainers": [
"your-github-username"
]
}Then, authenticate using GitHub.
Browse examples.
How to make a release?
A "release" on Glama is not the same as a GitHub release. To create a Glama release:
- Claim the server if you haven't already.
- Go to the Dockerfile admin page, configure the build spec, and click Deploy.
- Once the build test succeeds, click Make Release, enter a version, and publish.
This process allows Glama to run security checks on your server and enables users to deploy it.
How to add a LICENSE?
Please follow the instructions in the GitHub documentation.
Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear on the server after some time, you can manually trigger a new scan using the MCP server admin interface.
How to sync the server with GitHub?
Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.
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.
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