Point Gecko at an OpenAPI spec; get first-call-correct, auth-hidden agent tools.
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Average 4.2/5 across 1 of 1 tools scored.
Having only one tool eliminates any possibility of confusion, making its purpose immediately clear.
The single tool name 'comprehend_api' follows a clean verb_noun pattern, consistent within the set.
With only one tool, the server feels minimal for a general-purpose API comprehension service, though it could be sufficient for a narrow use case.
The tool covers the full workflow of API comprehension, from submitting a URL to receiving agent-ready artifacts, with no obvious missing operations within its stated scope.
comprehend_apiAInspectSubmit an API's OpenAPI URL (or a human docs page URL with from_docs=true) and get it comprehended into first-call-correct agent tools — no integration code. Returns the API name, its usable tools, agent-native artifacts (llms.txt / gecko.json / tools.md), and self-host next steps. Comprehends and returns to YOU only: it does not host, publicly list, or register your API.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The API's OpenAPI spec URL (or a docs page URL if from_docs). | |
| from_docs | No | Recover the surface from a human docs page instead of an OpenAPI spec. Results are quarantined pending review. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full disclosure responsibility. It explicitly states the tool does not host, publicly list, or register the API, and that docs-derived results are quarantined pending review. This gives important behavioral expectations beyond the simple action.
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, front-loaded with the core action, then outputs, then privacy caveat. Every sentence adds distinct value without redundancy, achieving excellent conciseness.
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's simplicity (2 params, no output schema), the description covers the return value (API name, tools, artifacts, next steps) and key caveats (quarantine, no hosting). It could mention error handling or output format specifics, but it is sufficiently complete for an agent to invoke 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?
The input schema already fully describes both parameters (url and from_docs), so the description adds minimal parameter-level detail. It does clarify that from_docs=true switches to documentation recovery, slightly extending the schema, but overall the schema does the heavy lifting.
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 that the tool takes an OpenAPI URL (or docs URL with from_docs=true) and converts it into usable agent tools. It uses specific terms like 'first-call-correct' and lists concrete outputs, making the purpose unambiguous even without sibling comparisons.
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 provides clear context on what inputs to submit (OpenAPI spec URL, or docs page with from_docs=true) and notes that docs-based results are quarantined. It lacks explicit 'when not to use' guidance, but the intended use case is well-defined.
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
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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