TopFood publishes digital restaurant menus with QR codes. Give it a menu (text, photo transcription, or description) and it creates a live, shareable menu page the restaurant owner can claim for free — no signup required upfront.
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Usage analytics
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Average 4.7/5 across 2 of 2 tools scored.
The two tools have clearly separate purposes: one creates a restaurant menu and the other reports platform issues. There is no overlap or ambiguity in choosing between them.
Both tool names use the same snake_case verb_noun pattern: create_restaurant_menu and report_problem. Naming is consistent and predictable.
With only two tools, the server feels very thin, but each tool serves a distinct role: one primary domain operation and one generic feedback channel. It is borderline but not unreasonable for such a narrow purpose.
The domain surface only supports creating a menu; there are no tools to list, update, retrieve, or delete menus. Common lifecycle actions would cause obvious agent failures, and the report_problem tool is only a manual fallback rather than a real management operation.
report_problemReport a problem with TopFoodAInspectSends a bug report or product feedback about TopFood to the maintainers. Use it when a tool call fails unexpectedly, when the published menu is wrong or incomplete, or when the restaurant owner tells you something is broken or missing. Do NOT use it for input that a tool already rejected with a clear explanation — fix the input and retry instead. Describe what was attempted, what happened, and what was expected. Reports are read by people and there is no reply channel: tell the user the report was sent, and that they should include an email address in the message if they want an answer.
| Name | Required | Description | Default |
|---|---|---|---|
| message | Yes | What happened, what was expected, and what the user was doing. Write it for a developer who cannot see this conversation. Include the user's own words when they reported it. | |
| category | Yes | bug = something is broken; missing_feature = something the user needs does not exist; confusing = it works but was unclear; pricing = about plans or cost; other = anything else. | |
| claim_url | No | The claim link returned by create_restaurant_menu, when the report is about a menu that was created in this conversation. It attaches the report to that restaurant. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are all false and minimal, so the description carries the burden. It discloses that reports are read by people, there is no reply channel, and the user should be told the report was sent. It even advises including an email address for a response. This is rich behavioral context beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, each earning its place: first defines purpose, second gives usage rules, third explains human-read behavior and follow-up steps. It is front-loaded and free of fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 3-parameter tool with no output schema and sparse annotations, the description covers purpose, usage, behavioral notes, and user-facing instructions. It doesn't explicitly state the return value, but the instruction to tell the user the report was sent implies the outcome. Minor gap: no mention of idempotency or retry behavior, but not essential here.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds value by instructing what to include in the message ('what was attempted, what happened, what was expected') and advising to include an email address, which enriches the meaning of the message parameter without repeating schema details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Sends a bug report or product feedback about TopFood to the maintainers.' It clearly distinguishes from the sibling tool create_restaurant_menu, which is about creating menus rather than reporting issues.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit when-to-use scenarios are listed ('when a tool call fails unexpectedly', 'published menu is wrong', etc.) and a direct exclusion is given: 'Do NOT use it for input that a tool already rejected with a clear explanation — fix the input and retry instead.' This provides strong decision guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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