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Paper Search MCP

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read_semantic_paper

Extract text content from Semantic Scholar papers using various identifiers like DOI, arXiv, or PMID to access research material for analysis.

Instructions

Read and extract text content from a Semantic Scholar paper.

Args: paper_id: Semantic Scholar paper ID, Paper identifier in one of the following formats: - Semantic Scholar ID (e.g., "649def34f8be52c8b66281af98ae884c09aef38b") - DOI: (e.g., "DOI:10.18653/v1/N18-3011") - ARXIV: (e.g., "ARXIV:2106.15928") - MAG: (e.g., "MAG:112218234") - ACL: (e.g., "ACL:W12-3903") - PMID: (e.g., "PMID:19872477") - PMCID: (e.g., "PMCID:2323736") - URL: (e.g., "URL:https://arxiv.org/abs/2106.15928v1") save_path: Directory where the PDF is/will be saved (default: './downloads'). Returns: str: The extracted text content of the paper.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paper_idYes
save_pathNo./downloads

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The MCP tool registration and implementation of `read_semantic_paper` which calls the `semantic_searcher.read_paper` helper method.
    @mcp.tool()
    async def read_semantic_paper(paper_id: str, save_path: str = "./downloads") -> str:
        """Read and extract text content from a Semantic Scholar paper. 
    
        Args:
            paper_id: Semantic Scholar paper ID, Paper identifier in one of the following formats:
                - Semantic Scholar ID (e.g., "649def34f8be52c8b66281af98ae884c09aef38b")
                - DOI:<doi> (e.g., "DOI:10.18653/v1/N18-3011")
                - ARXIV:<id> (e.g., "ARXIV:2106.15928")
                - MAG:<id> (e.g., "MAG:112218234")
                - ACL:<id> (e.g., "ACL:W12-3903")
                - PMID:<id> (e.g., "PMID:19872477")
                - PMCID:<id> (e.g., "PMCID:2323736")
                - URL:<url> (e.g., "URL:https://arxiv.org/abs/2106.15928v1")
            save_path: Directory where the PDF is/will be saved (default: './downloads').
        Returns:
            str: The extracted text content of the paper.
        """
        try:
            return semantic_searcher.read_paper(paper_id, save_path)
        except Exception as e:
            print(f"Error reading paper {paper_id}: {e}")
            return ""
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions downloading and saving PDFs ('Directory where the PDF is/will be saved'), implying network and file system operations, but lacks details on permissions, rate limits, error handling, or whether it's read-only/destructive. The description adds some context but is insufficient for a tool with no annotations.

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 well-structured with clear sections (Args, Returns) and uses bullet points for paper_id formats. It is appropriately sized, but the long list of formats could be slightly condensed. Every sentence adds value, making it efficient.

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

Completeness4/5

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

Given no annotations, 0% schema coverage, and an output schema (returns 'str'), the description does well by detailing parameters and the return value. However, it lacks behavioral context (e.g., network usage, errors) and sibling differentiation, leaving minor gaps for a tool with no annotations.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate. It provides comprehensive details on 'paper_id' formats (8 examples with prefixes) and explains 'save_path' as a directory for PDF storage with a default value. This adds significant meaning beyond the bare schema.

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

Purpose5/5

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 verbs ('Read and extract text content') and resource ('from a Semantic Scholar paper'). It distinguishes itself from sibling tools like 'download_semantic' and 'search_semantic' by focusing on content extraction rather than downloading or searching.

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?

No explicit guidance is provided on when to use this tool versus alternatives. With many sibling tools for different sources (e.g., 'read_arxiv_paper', 'read_pubmed_paper'), the description does not clarify when Semantic Scholar is preferred or what distinguishes it from other paper-reading tools.

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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