scholar

name

scholar

description

A freely accessible web search engine that indexes the full text or metadata of scholarly literature across an array of publishing formats and disciplines. It provides comprehensive academic research capabilities including paper search with keyword-based queries returning titles, authors, abstracts, citation counts, publication years and access links. Advanced search supports filtering by author names and publication year ranges. It also offers detailed author profile lookups with academic metrics including h-index, i10-index, total citations, research interests, and major publications. Suitable for academic research, literature reviews, citation analysis, and trend studies.

Scholar

Use this skill to answer questions that require academic literature search, citation data, or author profile information.

Setup

Check whether the agent-gw Python SDK is available in the current Python environment, and install it only if the check fails:

python3 -c "import agent_gw" || python3 -m pip install "$(curl -s https://cdn.kimi.com/agentgw/pysdk/manifest.json | python3 -c "import json,sys; print(json.load(sys.stdin)['latest']['url'])")"

The SDK needs an API key from api_key=..., KIMI_API_KEY, or ~/.kimi/agent-gw.json.

Workflow

1. Run python3 scripts/scholar_tool.py describe from the plugin directory to call get_data_source_desc({"name": "scholar"}).
2. Read the returned Markdown carefully. It contains the overall data source rules, academic search formats, global constraints, and each API's description, required parameters, optional parameters, defaults, and allowed values.
3. Select the API that best matches the user's question, such as paper search, advanced paper search, or author profile lookup.
4. Build params exactly from the Markdown requirements. Use documented keyword, author, publication year, pagination, profile, citation, or access-link fields only when the API supports them.
5. Use python3 scripts/scholar_tool.py call to call call_data_source_tool.
6. If the call fails, explain the failure reason from the response.
7. If the call succeeds, save any returned files first, then answer using resp.result.assistant; ignore resp.result.user unless display content is specifically needed.

Script

Use the bundled script from the plugin directory:

python3 scripts/scholar_tool.py describe

After reading the Markdown and selecting an API:

python3 scripts/scholar_tool.py call \
  --api-name "<api name from markdown>" \
  --params-json '{"required_param":"value"}'

For larger params, write a JSON object and pass --params-file path/to/params.json.

The script:

• sends {"name": "scholar"} to get_data_source_desc
• sends {"data_source_name": "scholar", "api_name": ..., "params": ...} to call_data_source_tool
• prints failure messages from error.user or error.assistant
• saves returned files to each files[].name path returned by the data source
• prints the joined result.assistant texts on success

Expected call_data_source_tool response shape:

{
    "is_success": bool,
    "result": {"user": list[str], "assistant": list[str]} | None,
    "error": {"user": list[str], "assistant": list[str]} | None,
    "files": [{"name": str, "content": str}],
}

When files are returned, name is the file path or name to write. The path is usually dictated by the selected API's params in the Markdown docs. If an API does not need files, the response normally has no files to save.