AI Consultant MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| VERBOSE_LOGGING | No | Set to 'true' or '1' to enable detailed logging | false |
| OPENROUTER_API_KEY | Yes | Your OpenRouter API key (required) |
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 |
|---|---|
| consult_aiA | Consult with an AI model via OpenRouter. You can either specify a model or let the system auto-select based on your task. For sequential multi-model consultation, use the 'models' parameter to specify multiple models. |
| list_modelsB | List all available AI models with their descriptions and best use cases |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
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: consult_ai is for executing AI consultations, while list_models is for retrieving model information. There is no overlap in functionality, making it impossible for an agent to confuse them.
Both tools follow a consistent verb_noun pattern (consult_ai, list_models) with clear, descriptive names. The naming convention is uniform and predictable across the set.
With only two tools, the server feels under-scoped for an 'AI Consultant' domain. While the tools cover consultation and model listing, there are likely missing operations like managing consultation history, configuring model parameters, or handling feedback, making the set feel incomplete for the stated purpose.
For an AI consultant server, the tool surface is severely incomplete. It lacks essential operations such as saving or retrieving past consultations, adjusting consultation settings, or providing feedback on model performance. The current tools only cover the most basic consultation flow, leaving significant gaps that will hinder agent workflows.