authenticated-multi-llm-agent
Server Details
Google-OAuth-gated LLM gateway: verify a Google ID token, then run a Gemini (Vertex AI) completion f
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4/5 across 1 of 1 tools scored.
With only one tool, there is no possibility of confusion between tools. The tool's purpose is clearly defined.
With a single tool, naming consistency is not an issue. The verb 'invoke' is clear but could be more descriptive (e.g., 'invoke_llm'), so it's not a perfect 5.
A single tool is extremely limited for a server that claims to be a multi-LLM agent. Essential operations like listing models, managing contexts, or handling different authentication flows are missing, making the tool count severely inadequate.
The tool surface is critically incomplete. The server only provides one invocation endpoint, lacking any model management, configuration, history, or multi-LLM orchestration. The name 'multi-llm' is misleading.
Available Tools
1 toolinvokeAInspect
Google-OAuth-gated LLM gateway: verify a Google ID token, then run a Gemini (Vertex AI) completion for the verified caller.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | JSON request for this capability (the same body you'd send as an A2A message). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions OAuth verification and gateway behavior, but lacks details on failure modes, rate limits, or response format. For a verification and generation tool, additional transparency would improve trust.
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 a single sentence that efficiently conveys the tool's purpose and core behavior with no wasted words. It is well 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 one parameter and no output schema, the description covers the essential aspects: OAuth gating and Gemini completion. It does not explain the output or error handling, but for a simple tool, it is reasonably complete.
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 coverage is 100% with a single 'input' parameter. The description adds context that the input is a JSON request like an A2A message, but does not elaborate on structure or constraints. This adds marginal value beyond the schema description.
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 is a Google-OAuth-gated LLM gateway that verifies a Google ID token and runs a Gemini completion. It specifies the verb (verify, run) and resource (Gemini/Vertex AI) with sufficient detail. No sibling tools exist, so no differentiation needed.
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 the tool is for authenticated callers using Google OAuth tokens and that it performs a completion. It provides clear context but does not explicitly state when not to use it or mention alternatives. However, with no sibling tools, the guidance is effective.
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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{
"$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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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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