zugabot
Server Details
Paid dev services for AI agents: code review, bug-fix, tests, docs, audits. USDC on Base x402.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.8/5 across 3 of 3 tools scored.
Each tool serves a unique, non-overlapping purpose: list_services for browsing all services, preview for a sample output, and service_info for specific details and payment instructions. No ambiguity.
Tools use lowercase with underscores; two follow a verb_noun pattern (list_services, service_info) while preview is a single verb. Minor inconsistency but still clear and readable.
With only 3 tools, the server is tightly scoped to its purpose of browsing and previewing services. No unnecessary tools; the count is minimal but sufficient.
Covers all basic needs: listing, previewing, and getting details. A tool to actually execute or purchase a service might be expected, but the server appears informational only, so gaps are minor.
Available Tools
3 toolslist_servicesAInspect
List all Zugabot paid developer services with prices and x402 pay endpoints. Free.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description only states the tool lists data, implying a read operation. It does not disclose any behavioral traits like authentication needs, rate limits, or idempotency. For a simple list tool, this is adequate but minimal.
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?
Two concise sentences, each providing essential information. No wasted words, and the key details are front-loaded.
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?
Given the tool has no parameters and an output schema is present, the description is largely complete. It could be improved by noting that the output schema describes the return structure, but the description itself adequately covers what the tool does.
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?
The tool has no parameters, and schema coverage is trivially 100%. The description adds value by specifying what is listed (prices, x402 pay endpoints), which is not in the schema, earning a baseline 4.
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 clearly states the tool lists all paid developer services with prices and endpoints, using a specific verb and resource. It distinguishes itself from siblings 'preview' and 'service_info' which likely operate on single items.
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?
The description implies usage with 'List all' and mentions 'Free', but does not explicitly state when to use this tool instead of siblings 'preview' or 'service_info', nor provide any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
previewAInspect
Show a representative sample of a services output so you can judge quality before paying. Free; canned sample, not a live run.
| Name | Required | Description | Default |
|---|---|---|---|
| service | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses it's a canned sample, not a live run, and free. However, it does not detail output format, sample size, or any limitations, leaving some behavioral aspects unspecified.
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?
Two sentences, concise and front-loaded. No wasted words; every sentence adds value.
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?
Given a simple tool with one parameter and an existing output schema, the description covers purpose, cost, and nature. It is sufficiently complete for an agent to understand and use the tool correctly.
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?
Only one parameter 'service' with 0% schema coverage. The description implies it takes a service identifier but does not explicitly describe valid values or format. The meaning is inferable but not fully fleshed out.
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 clearly states the tool's purpose: 'Show a representative sample of a services output so you can judge quality before paying.' It distinguishes from siblings (list_services lists services, service_info gives info) by focusing on a sample output.
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?
It provides clear context: use before paying to judge quality, and notes it's free and a canned sample. It implicitly sets expectations but does not explicitly mention when not to use or alternative tools beyond the sibling context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
service_infoBInspect
Full detail and payment instructions for a single Zugabot service. Free.
| Name | Required | Description | Default |
|---|---|---|---|
| service | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description only adds 'Free' as a behavioral trait. It omits details on idempotency, side effects, or authentication requirements.
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 one sentence plus a standalone word, efficiently conveying purpose and cost. It is front-loaded and without unnecessary 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?
Given the presence of an output schema, the description need not detail return values, but it still lacks guidance on parameter input and prerequisites, making it adequate but not comprehensive.
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?
With 0% schema coverage, the description vaguely indicates 'service' is an identifier but does not specify format, valid values, or examples, failing to compensate for the lack of parameter documentation.
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 clearly states it provides 'full detail and payment instructions for a single Zugabot service,' with the word 'single' distinguishing it from sibling tools like list_services.
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?
The description implies use when needing detailed info for one service, but does not explicitly mention when not to use or direct to alternatives like list_services or preview.
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.
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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
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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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