GPT-4o: GPT-4o (“o” for “omni”) is our versatile, high-intelligence flagship model. It accepts both.
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Average 2.9/5 across 1 of 1 tools scored.
With only one tool, there is no possibility of overlapping purposes or misselection. The tool's purpose is singular and clearly described.
The single tool follows a clear verb_noun pattern ('post_chat_completions'), and there are no conflicting naming conventions.
One tool is below the typical 3-15 range, but it is reasonable for a narrowly scoped chat-completion endpoint. The server is minimal but not unreasonably so.
For the server's apparent purpose—creating chat completions—the single tool covers the core operation fully. There are no obvious missing lifecycle steps or dead ends within that narrow scope.
post_chat_completionsChat CompletionsCInspectCreates a model response for the given chat conversation Billing per call: Credits: metered (~0 avg).
| Name | Required | Description | Default |
|---|---|---|---|
| body | No | JSON request body. Example: {"model":"GPT-4o","stream":false,"messages":[{"role":"user","content":"There are ten birds in a tree. A hunter shoots one. How many are left in the tree?"}]} |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It mentions billing ('Credits: metered (~0 avg)') but lacks details on rate limits, latency, error behavior, or data handling. Does not describe what happens with streaming or potential side effects beyond cost.
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?
Single sentence with a billing note. Efficient and focused, but omits critical usage details. Structure is clear and front-loaded with the primary action.
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 complexity (nested object body, no output schema, no annotations), description is too sparse. It lacks information on response format, potential errors, streaming behavior, or authentication. Billing note is helpful but insufficient for a complex API call tool.
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 provides 100% coverage with a detailed example in 'body' description. However, the description adds minimal semantic value beyond the example, mostly indicating this is the request body. Baseline 3 applies since schema covers everything, but no extra meaning added.
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?
Clear verb+resource: 'Creates a model response for the given chat conversation.' Distinguishes from alternatives like editing or listing completions. Slight ambiguity about whether it supports streaming or non-streaming, but example clarifies.
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?
No guidance on when to use or when not to use alternatives. No context on prerequisites like authentication or API key, or whether to prefer other tools for specific tasks. The billing note hints at cost but not usage scenarios.
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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