structured-output-agent
Server Details
Turn a prompt + a field schema into validated, typed JSON (Instructor over Gemini 2.5 Flash on Verte
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 3.6/5 across 1 of 1 tools scored.
Only one tool exists, so there is no possibility of ambiguity between tools. The tool's purpose is clearly defined.
With a single tool, naming consistency is trivially maintained. The verb-based name 'invoke' is appropriate for the action performed.
One tool for a focused purpose is reasonable. While the server could potentially benefit from additional tools (e.g., for different models), the current count is not inappropriate given the narrow scope.
The tool fully covers the stated purpose: converting a prompt and schema into validated JSON. There are no obvious gaps in the functionality for this domain.
Available Tools
1 toolinvokeAInspect
Turn a prompt + a field schema into validated, typed JSON (Instructor over Gemini 2.5 Flash on Vertex AI).
| 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?
With no annotations provided, the description carries full burden. It discloses use of a generative AI model (Gemini 2.5 Flash on Vertex AI) and mentions validation, but does not address rate limits, latency, error modes, or failure cases (e.g., invalid schema, timeout). Partial transparency but incomplete for safe invocation.
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, highly efficient sentence with no redundancy. Every word contributes to understanding the tool's core function. Perfectly 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 a generative tool with no output schema and one parameter, the description lacks critical context: return format (only described as 'validated, typed JSON' but no detail), error handling, or typical usage. The agent would need to guess important details like how to specify the field schema within the JSON request.
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 exists with 100% schema description coverage, so baseline is 3. The description adds the context that the input is like an A2A message, which is helpful but assumes external knowledge. The tool description mentions 'prompt + field schema' but the parameter description does not explain how to structure them; thus, limited additional value beyond schema.
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 action (Turn a prompt + field schema into JSON), specifies the result (validated, typed JSON), and names the underlying technology (Instructor over Gemini on Vertex AI). This provides a specific verb and resource, making the tool's purpose unmistakable even without siblings.
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 for generating structured JSON from a prompt and schema, but does not explicitly state when to use this tool versus alternatives or when not to use it. No exclusions or alternative suggestions are provided, leaving the agent to infer applicability.
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
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