Stochastic Thinking 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 |
|---|---|
| stochasticalgorithmC | A tool for applying stochastic algorithms to decision-making problems. Supports various algorithms including:
Each algorithm provides a systematic approach to handling uncertainty in decision-making. |
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 1 tool
With only one tool, there is no ambiguity or overlap between tools. The tool 'stochasticalgorithm' clearly encompasses all stochastic algorithm applications for decision-making, leaving no room for confusion or misselection.
A single tool name follows a consistent pattern by using a descriptive compound term 'stochasticalgorithm'. There are no other tools to compare against, so naming consistency is inherently perfect with no deviations or mixed conventions.
The server has only one tool, which feels too thin for the broad scope implied by covering multiple stochastic algorithms like MDPs, MCTS, and Bayesian Optimization. A single tool handling such diverse algorithms may lack granularity and could be better served by multiple specialized tools for clearer functionality.
The tool covers various stochastic algorithms, but as a single tool, it may lack completeness in terms of specific operations like configuring, running, or analyzing results for each algorithm separately. There are no obvious gaps in the algorithms listed, but the surface might be too monolithic for effective agent use without more detailed tool breakdowns.