research-mentor-rag
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
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| research_bootstrapA | Load the compact 1200-paper RAG runtime and L0-L5 workflow status. |
| research_rag_queryC | Query the local embedding-free 1200-paper RAG. |
| research_routeC | Run mandatory multi-head stage retrieval and return next action/correction. |
| research_mentorD | Combine stage RAG retrieval with 12-Skill specialist method selection. |
| research_frontier_searchC | Run a dated live OpenAlex+Crossref frontier search for nearest papers, topics and gaps. |
| research_controllerC | Auto-detect a research stage, run mentor RAG, and require live frontier search for novelty before answering. |
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 6 tools
Each tool targets a distinct aspect of the research workflow, but research_route and research_mentor both involve stage retrieval. Their descriptions clarify different purposes: route returns next actions while mentor selects specialist methods, so most agents can differentiate them.
All tools share the consistent 'research_' prefix, and most use action-oriented names like bootstrap, route, and mentor. The mix of verb and noun forms (e.g., controller vs. frontier_search) is a minor deviation from a strict verb_noun pattern.
Six tools are well-scoped for a research mentor RAG system, covering initialization, direct query, routing, mentoring, live search, and orchestration. This is a reasonable number that avoids both thinness and bloat.
The tool set covers the full workflow from bootstrapping the RAG runtime to answering after a novelty search, including a controller that orchestrates the process. Minor gaps exist, such as no explicit tool for updating the RAG corpus, but core operations are complete.