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by DaniManas

extract_claims

Extract key claims and findings from research paper abstracts to identify core contributions and evidence. Use OpenAlex paper IDs to retrieve structured claim summaries for academic analysis.

Instructions

Extract key claims and findings from a paper's abstract.

Args: paper_id: The OpenAlex paper ID

Returns: Structured list of claims extracted from the paper

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paper_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The core handler function for the 'extract_claims' tool. It fetches the paper by ID, retrieves the abstract, formats the paper info and abstract, and provides instructions for extracting key claims, hypothesis, methodology, findings, and conclusions. The @mcp.tool() decorator also registers it as an MCP tool.
    @mcp.tool()
    def extract_claims(paper_id: str) -> str:
        """
        Extract key claims and findings from a paper's abstract.
    
        Args:
            paper_id: The OpenAlex paper ID
    
        Returns:
            Structured list of claims extracted from the paper
        """
        paper = fetcher.fetch_paper_by_id(paper_id)
    
        if "error" in paper:
            return paper["error"]
    
        abstract_text = fetcher.get_paper_abstract(paper)
    
        if abstract_text == "No abstract available":
            return "Cannot extract claims: No abstract available for this paper"
    
        result = f"**Paper:** {paper['title']}\n"
        result += f"**Authors:** {paper['authors']}\n"
        result += f"**Year:** {paper['publication_year']}\n\n"
        result += f"**Abstract:**\n{abstract_text}\n\n"
        result += f"**Instructions for claim extraction:**\n"
        result += f"Please analyze the abstract above and extract:\n"
        result += f"1. Main research question or hypothesis\n"
        result += f"2. Key methodology or approach\n"
        result += f"3. Primary findings or results\n"
        result += f"4. Main conclusions or implications\n"
    
        return result
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool extracts claims from an abstract, but doesn't describe how it works (e.g., NLP methods, confidence scores), what happens with invalid inputs, rate limits, or error handling. For a tool with zero annotation coverage, this leaves significant behavioral gaps.

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 appropriately sized and front-loaded with the core purpose in the first sentence. The Args and Returns sections are structured but slightly verbose for a single parameter; every sentence earns its place by clarifying inputs and outputs, though it could be more streamlined.

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 the tool's moderate complexity (extraction task), no annotations, and an output schema that handles return values, the description is reasonably complete. It covers purpose, parameters, and returns, but could improve by adding more behavioral context or usage guidelines to fully compensate for the lack of annotations.

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 0%, so the description must compensate. It adds meaning by specifying that paper_id is an 'OpenAlex paper ID', which clarifies the format beyond the schema's generic string type. However, it doesn't explain where to find this ID or provide examples, leaving some ambiguity for the single parameter.

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 specific action ('Extract key claims and findings') and target resource ('from a paper's abstract'), distinguishing it from siblings like get_paper_abstract (which retrieves the abstract) or search_papers (which searches for papers). It precisely defines the tool's function without being vague or tautological.

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

Usage Guidelines3/5

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

The description implies usage when you need claims from an abstract, but provides no explicit guidance on when to use this tool versus alternatives like compare_papers or find_research_gaps. It mentions the required paper_id parameter, which hints at prerequisites, but lacks clear when/when-not instructions or named alternatives for different use cases.

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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