ComputeSage server
# ComputeSage StackBench
Public metadata for the ComputeSage StackBench remote Model Context Protocol
(MCP) server. This repository does not contain the proprietary StackBench
server implementation.
[](https://glama.ai/mcp/servers/magnetism87/computesage-mcp)
[](https://glama.ai/mcp/servers/magnetism87/computesage-mcp)
## Connect
Remote MCP:
<https://mcp.computesage.com/mcp>
Website:
<https://computesage.com>
Documentation:
<https://computesage.com/mcp>
Official Registry:
`com.computesage/stackbench`
## Tools
- `search_evidence`
- `check_deployment_fit`
- `predict_performance`
- `recommend_deployment`
- `generate_launch_config`
## Metadata
[`server.json`](server.json) mirrors the public server identity and remote
transport published in the Official MCP Registry. Service availability,
current tool schemas, and tool behavior are defined by the live remote MCP
server and its documentation.
## Repository scope
This repository contains public discovery metadata only. It intentionally
contains no StackBench application source, infrastructure configuration,
credentials, private benchmark data, or deployment secrets.
TDQS
Scored across 5 tools
Each tool targets a distinct stage in the evidence-to-deployment workflow. check_deployment_fit and predict_performance are related but clearly separated by fit versus metrics, and recommend_deployment is the only economic recommendation tool.
All five tools use consistent lowercase snake_case verb_noun names with concrete action verbs (search, check, predict, recommend, generate), making the naming pattern predictable and readable.
Five tools is appropriate for a specialized advice-and-configuration server. Every tool serves a distinct step in the workflow without redundancy or unnecessary surface area.
The server covers the full advisory pipeline: evidence search, fit validation, performance prediction, recommendation, and launch configuration generation. It intentionally stops before executing deployment, which is consistent with its stated purpose.