Paper Search MCP

search_google_scholar

Search Google Scholar for academic papers by query, author, publication year range, and result count. Use filters to narrow results and retrieve scholarly articles for research or citation needs.

Instructions

Search Google Scholar for academic papers using web scraping

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "search_google_scholar"
  }
}

Input Schema

Name	Required	Description
`query`	Yes	Search query string
`author`	No	Author name filter
`yearLow`	No	Earliest publication year
`yearHigh`	No	Latest publication year
`maxResults`	No	Maximum number of results to return

Schema Changelog

Changes observed during successful MCP inspections.

First observedDec 29, 2025

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations provided, the description carries full burden but only mentions 'web scraping' without detailing behavioral traits like rate limits, authentication needs, or potential risks (e.g., blocking). It lacks information on response format, error handling, or operational constraints, leaving 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 a single, efficient sentence with zero waste—it directly states the tool's function and method. It's appropriately sized and front-loaded, making it easy to grasp immediately without unnecessary elaboration.

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?

Given the complexity of a search tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain return values, error cases, or how results are structured, which is critical for an agent to use the tool effectively. The 'web scraping' hint is insufficient for full context.

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 fully documents all 5 parameters. The description adds no additional meaning beyond the schema, such as query syntax examples or interactions between parameters like yearLow and yearHigh. Baseline 3 is appropriate as the schema handles parameter documentation.

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 action ('search') and resource ('Google Scholar for academic papers'), making the purpose understandable. However, it doesn't differentiate from sibling tools like search_arxiv or search_pubmed, which perform similar academic searches on different platforms, so it misses full distinction.

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, such as search_arxiv for physics papers or search_pubmed for medical literature. It mentions 'web scraping' but doesn't explain implications or exclusions, leaving usage context vague.

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