mcp-neurolora
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 |
|---|---|
| collect_codeC | Collect all code from a directory into a single markdown file |
| install_base_serversC | Install base MCP servers to the configuration |
| analyze_codeC | Analyze code using OpenAI API (requires your API key). The analysis may take a few minutes. So, wait please. |
| create_github_issuesC | Create GitHub issues from analysis results. Requires GitHub token. |
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 4 tools
Each tool has a clearly distinct purpose with no overlap: analyze_code for code analysis, collect_code for file aggregation, create_github_issues for issue creation, and install_base_servers for server installation. The descriptions clearly differentiate their functions, eliminating any ambiguity.
All tool names follow a consistent verb_noun pattern (e.g., analyze_code, collect_code, create_github_issues, install_base_servers). The naming is uniform and predictable, using snake_case throughout with clear action-object pairs.
With only 4 tools, the set feels thin for a server named 'mcp-neurolora', which suggests a broader scope related to code analysis or AI workflows. While the tools cover specific tasks, the count is borderline low, potentially leaving gaps in functionality for the implied domain.
The tool set has significant gaps for a code analysis or AI workflow server. It lacks core operations like retrieving or updating issues, managing analysis results, or handling configurations beyond installation. This incomplete surface will likely cause agent failures in extended workflows.