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by prtc

get_author_papers

Retrieve all published papers by a specific author from NASA ADS, including citations and publication details. Sort results by date or citation count to analyze research output.

Instructions

Find all papers by a specific author. Returns list of papers with citations and publication details.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
authorYesAuthor name (e.g., 'Coelho, P.' or 'Coelho, Paula')
max_resultsNoMaximum number of results (default: 20, max: 100)
sortNoSort by 'date' or 'citation_count'date

Implementation Reference

  • The main handler function that executes the tool logic: queries the NASA ADS API for papers by the given author, formats the results with titles, years, citations, and bibcodes, and returns them as TextContent.
    async def get_author_papers(author: str, max_results: int, sort: str) -> list[TextContent]:
        """Get papers by a specific author."""
        try:
            # Prepare sort parameter
            sort_param = "date desc" if sort == "date" else "citation_count desc"
            
            # Search for author
            papers = ads.SearchQuery(
                author=author,
                fl=["bibcode", "title", "year", "citation_count", "pubdate"],
                rows=min(max_results, 100),
                sort=sort_param,
            )
            
            # Format results
            results = []
            total_citations = 0
            for i, paper in enumerate(papers, 1):
                citations = paper.citation_count or 0
                total_citations += citations
                results.append(
                    f"{i}. {paper.title[0] if paper.title else 'No title'} ({paper.year})\n"
                    f"   Citations: {citations} | Bibcode: {paper.bibcode}\n"
                )
            
            if not results:
                return [TextContent(
                    type="text",
                    text=f"No papers found for author: {author}"
                )]
            
            response = (
                f"Found {len(results)} papers by '{author}' "
                f"(Total citations: {total_citations}):\n\n"
                + "\n".join(results)
            )
            return [TextContent(type="text", text=response)]
        
        except Exception as e:
            logger.error(f"Error getting author papers: {e}")
            return [TextContent(
                type="text",
                text=f"Error getting author papers: {str(e)}"
            )]
  • Pydantic/JSON schema defining the input parameters for the get_author_papers tool: author (required), max_results (default 20), sort (date or citation_count, default date). Defines validation and documentation.
    inputSchema={
        "type": "object",
        "properties": {
            "author": {
                "type": "string",
                "description": "Author name (e.g., 'Coelho, P.' or 'Coelho, Paula')",
            },
            "max_results": {
                "type": "integer",
                "description": "Maximum number of results (default: 20, max: 100)",
                "default": 20,
            },
            "sort": {
                "type": "string",
                "description": "Sort by 'date' or 'citation_count'",
                "enum": ["date", "citation_count"],
                "default": "date",
            },
        },
        "required": ["author"],
    },
  • Registration of the get_author_papers tool in the MCP server's list_tools() function, including name, description, and input schema.
    Tool(
        name="get_author_papers",
        description=(
            "Find all papers by a specific author. "
            "Returns list of papers with citations and publication details."
        ),
        inputSchema={
            "type": "object",
            "properties": {
                "author": {
                    "type": "string",
                    "description": "Author name (e.g., 'Coelho, P.' or 'Coelho, Paula')",
                },
                "max_results": {
                    "type": "integer",
                    "description": "Maximum number of results (default: 20, max: 100)",
                    "default": 20,
                },
                "sort": {
                    "type": "string",
                    "description": "Sort by 'date' or 'citation_count'",
                    "enum": ["date", "citation_count"],
                    "default": "date",
                },
            },
            "required": ["author"],
        },
    ),
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return format but doesn't cover important aspects like whether this is a read-only operation, potential rate limits, authentication requirements, error conditions, or pagination behavior. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured in two sentences that convey the core purpose and return format without unnecessary elaboration. It's appropriately sized for this type of lookup tool, though it could potentially be more front-loaded with key behavioral information given the lack of annotations.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the returned paper objects contain beyond 'citations and publication details', doesn't mention error handling, and provides no behavioral context. Given the complexity of academic paper data and the lack of structured metadata, more comprehensive guidance would be helpful.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema (like clarifying author name formats or result limitations). Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verb ('Find') and resource ('papers by a specific author'), and mentions the return format ('list of papers with citations and publication details'). However, it doesn't explicitly differentiate from sibling tools like 'get_author_metrics' or 'search_papers', which might have overlapping functionality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'search_papers' or 'get_author_metrics'. It lacks context about prerequisites, exclusions, or comparative use cases with sibling tools, leaving the agent to infer usage based on tool names alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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