MCP Server Neurolorap
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 |
|---|---|
| code_collectorC | Collect code from files into a markdown document |
| project_structure_reporterC | Generate a report of project structure metrics |
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 clearly distinct purposes: code_collector focuses on extracting code content into a markdown document, while project_structure_reporter generates metrics about the project's structure. There is no overlap in functionality, making it easy for an agent to choose the right tool.
Both tools follow a consistent noun_verb pattern (code_collector and project_structure_reporter), using snake_case throughout. The naming is predictable and readable, with no deviations in style or convention.
With only 2 tools, the server feels thin for a domain like project analysis or code management. This minimal set may not cover essential operations such as code analysis, dependency checking, or file manipulation, limiting its utility for broader tasks.
Inferred domain is project/code analysis, but the tool surface is severely incomplete. It lacks basic CRUD operations (e.g., no tools for creating, updating, or deleting files), code quality checks, or integration with version control, leaving significant gaps that could cause agent failures.