Meta Prompt MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| ready_to_answerB | Use this tool when you already obtained or verified the final solution with at least two independent experts and are ready to present your final answer. |
| expert_modelC | Use this tool to communicate with an expert. Args: name: The name of the expert to communicate with. Required. instructions: The instructions to send to the expert. Required. output: The answer from the expert based on the instructions. Required. iteration: The number of experts you have consulted so far. Start with 1. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| meta_model_prompt |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 2 tools
The two tools have completely distinct purposes: expert_model is for consulting experts and obtaining their responses, while ready_to_answer is for signaling completion after verification with multiple experts. There is no overlap in functionality, making it impossible to confuse them.
The naming is mixed: expert_model uses snake_case but is a noun-based name, while ready_to_answer uses snake_case with a verb phrase. There is no consistent verb_noun pattern, but both names are readable and descriptive of their functions.
With only 2 tools, the server feels under-scoped for a 'Meta Prompt' purpose, which suggests broader capabilities. The tools cover a narrow workflow (consult experts and signal readiness), lacking operations like managing experts, tracking iterations, or handling errors, making the set feel incomplete for the domain.
Inferred domain is meta-prompting or expert consultation, but there are significant gaps: no tools to list available experts, modify expert instructions, handle errors in consultations, or manage the consultation process beyond the two provided steps. This will likely cause agent failures when trying to perform full workflows.