ResearchTwin
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| RESEARCHTWIN_URL | No | The base URL for a local or self-hosted ResearchTwin instance. |
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
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_researchersA | List all researchers registered on the ResearchTwin platform. Returns researcher slugs and display names. Use a slug with other tools to explore a specific researcher's profile, papers, datasets, and repositories. |
| get_profileA | Get a researcher's profile with S-Index score and summary metrics. Args: slug: Researcher identifier (e.g. 'martin-frasch'). Use list_researchers to find valid slugs. Returns structured profile with S-Index, h-index, paper count, citation count, and links to papers/datasets/repos endpoints. |
| get_contextA | Get comprehensive research context for a researcher including all data source metrics. Args: slug: Researcher identifier (e.g. 'martin-frasch'). Returns S-Index, paper impact, source connection status (Semantic Scholar, Google Scholar, GitHub, Figshare), dataset QIC scores, and repo QIC scores. More detailed than get_profile — use this when you need the full picture. |
| get_papersA | Get a researcher's publications with citation counts. Args: slug: Researcher identifier. Returns papers from Semantic Scholar and Google Scholar (merged, deduplicated) with titles, years, citation counts, and URLs. |
| get_datasetsA | Get a researcher's datasets with QIC (Quality x Impact x Collaboration) scores. Args: slug: Researcher identifier. Returns Figshare datasets with DOIs, download/view counts, and QIC scores computed using FAIR-based quality assessment. |
| get_reposA | Get a researcher's code repositories with QIC scores. Args: slug: Researcher identifier. Returns GitHub repositories with stars, forks, language, and QIC scores computed using FAIR-based quality assessment. |
| discoverA | Search across all researchers for papers, datasets, or repositories matching a keyword. Args: query: Search keyword (e.g. 'fetal', 'machine learning', 'turbulence'). type: Optional filter — 'paper', 'dataset', or 'repo'. Leave empty to search all types. Returns matching items across all registered researchers, sorted by relevance. This is the primary tool for cross-researcher discovery. |
| get_network_mapA | Get geographic affiliations for all researchers in the network. Returns researchers with their institutional affiliations and coordinates, sourced from ORCID and Semantic Scholar. Useful for understanding the geographic distribution of the research network. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| about | Information about the ResearchTwin platform and its API. |
TDQS
Scored across 8 tools
Each tool has a clearly distinct purpose with no ambiguity. The tools are well-separated by function: discovery across researchers, getting different types of researcher-specific content (context, datasets, papers, repos, profile), listing researchers, and network mapping. The descriptions explicitly differentiate similar tools like get_context vs get_profile.
All tools follow a consistent verb_noun pattern with clear, descriptive names. The naming convention is perfectly uniform: discover, get_context, get_datasets, get_network_map, get_papers, get_profile, get_repos, list_researchers. Every tool name immediately communicates its function.
With 8 tools, this is well-scoped for a research platform. Each tool earns its place by covering distinct aspects of researcher data exploration: discovery, listing, detailed profiling, and specific content types. The count is appropriate for the domain without being overwhelming or insufficient.
The tool surface provides complete coverage for exploring a research network. It includes discovery across researchers, listing all researchers, comprehensive profiling (both summary and detailed context), and access to all research outputs (papers, datasets, repositories). The network map adds valuable geographic context, creating a well-rounded set with no obvious gaps.