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ClawyPro

Clawy MCP Server

by ClawyPro

semantic_scholar_search

Search academic papers on Semantic Scholar to find research by topic, author, or keyword, returning titles, abstracts, citations, and PDF links.

Instructions

Search academic papers on Semantic Scholar. Find research papers by topic, author, or keyword. Returns titles, abstracts, citation counts, and PDF links.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query for academic papers
limitNoNumber of results (default 10, max 100)
yearNoPublication year filter (e.g., '2024', '2023-2025')
fields_of_studyNoField filter (e.g., ['Computer Science', 'Medicine'])
open_access_onlyNoOnly return open access papers

Implementation Reference

  • Generic MCP tool handler registration that executes the tool logic via `gatewayRequest`.
    server.tool(
      tool.name,
      tool.description,
      tool.inputSchema.shape,
      async (params) => {
        const method = tool.method || "POST";
        const result = await gatewayRequest(method, tool.endpoint, params as Record<string, unknown>);
    
        if (result.error) {
          return {
            content: [{ type: "text" as const, text: `Error (${result.status}): ${result.error}` }],
            isError: true,
          };
        }
    
        const text = typeof result.data === "string"
          ? result.data
          : JSON.stringify(result.data, null, 2);
    
        return {
          content: [{ type: "text" as const, text }],
        };
      },
    );
  • Input schema definition for the `semantic_scholar_search` tool.
    inputSchema: z.object({
      query: z.string().describe("Search query for academic papers"),
      limit: z.number().optional().describe("Number of results (default 10, max 100)"),
      year: z.string().optional().describe("Publication year filter (e.g., '2024', '2023-2025')"),
      fields_of_study: z.array(z.string()).optional()
        .describe("Field filter (e.g., ['Computer Science', 'Medicine'])"),
      open_access_only: z.boolean().optional().describe("Only return open access papers"),
    }),
  • Registration of the `semantic_scholar_search` tool definition.
    export const semanticScholarTools: ToolDef[] = [
      {
        name: "semantic_scholar_search",
        description: "Search academic papers on Semantic Scholar. Find research papers by topic, author, or keyword. Returns titles, abstracts, citation counts, and PDF links.",
        inputSchema: z.object({
          query: z.string().describe("Search query for academic papers"),
          limit: z.number().optional().describe("Number of results (default 10, max 100)"),
          year: z.string().optional().describe("Publication year filter (e.g., '2024', '2023-2025')"),
          fields_of_study: z.array(z.string()).optional()
            .describe("Field filter (e.g., ['Computer Science', 'Medicine'])"),
          open_access_only: z.boolean().optional().describe("Only return open access papers"),
        }),
        endpoint: "/v1/semantic-scholar/search",
      },
    ];
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. While it mentions the return data (titles, abstracts, citation counts, PDF links), it doesn't cover important aspects like rate limits, authentication requirements, error conditions, or pagination behavior. For a search tool with no annotation coverage, this leaves significant gaps.

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

Conciseness5/5

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

The description is perfectly concise with two sentences that each earn their place: the first defines the tool's purpose and scope, the second specifies the return values. No wasted words, and the most important information (what it searches and what it returns) is front-loaded.

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

Completeness3/5

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

For a search tool with 5 parameters, 100% schema coverage, but no annotations and no output schema, the description provides adequate basic information about purpose and returns. However, it lacks behavioral context (rate limits, errors) and doesn't explain the relationship to sibling tools. The absence of an output schema means the description should ideally provide more detail about the return structure.

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 5 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain query syntax or field_of_study options). 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.

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 ('Search academic papers on Semantic Scholar'), resource ('academic papers'), and scope ('by topic, author, or keyword'). It distinguishes this tool from sibling tools like 'brave_search' or 'google_maps_places' by specifying the academic domain and Semantic Scholar platform.

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 'brave_search' for general web searches or 'llm_chat' for conversational research. It mentions what the tool does but offers no explicit when/when-not instructions or comparison to sibling 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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