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Serpapi Google Scholar

serpapi_google_scholar
Read-onlyIdempotent

Search Google Scholar for academic papers on <topic> — returns title, link, snippet, publication info, and citation count via SerpApi. Example: serpapi_google_scholar({ q: "graph neural networks", as_ylo: 2020, num: 10, _apiKey: "your-serpapi-key" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesSearch query / topic, e.g. "graph neural networks"
numNoMax results to return (default 10, max 20)
as_yhiNoOptional end year (results published up to this year), e.g. 2024
as_yloNoOptional start year (results published from this year onward), e.g. 2020
_apiKeyYesSerpApi API key (get one at serpapi.com)

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds that it uses an external service (SerpApi) and lists the return fields indicating a read operation. No contradictions with annotations. However, it does not mention potential rate limits or costs of the external API, which would add further transparency.

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 sentence with an example, no unnecessary words. It front-loads the action and purpose. Every part earns its place—very concise and structured.

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 lack of an output schema, the description compensates by listing return fields. It covers the main parameters via example. It does not discuss edge cases like empty results or pagination limits beyond the num max. Overall, it is adequately complete for a tool with full schema coverage and clear 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 100%, so baseline is 3. The description adds an example with parameter usage and mentions the topic placeholder, but it does not significantly enhance the meaning of individual parameters beyond what the schema already provides. The output field list is useful but not strictly parameter semantics.

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 action 'Search Google Scholar for academic papers' and specifies the resource (Google Scholar) and the return fields (title, link, snippet, publication info, citation count). It distinguishes from sibling tools like serpapi_google_jobs or deep_research by focusing on academic paper search.

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 does not explicitly provide when or when not to use this tool versus alternatives. The example implies typical usage, but there is no guidance on when to prefer this over other research tools like deep_research or other serpapi tools. Context of academic paper search is clear, but comparative guidance is missing.

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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TDQS

A3.8/5.0
Disambiguation3/5

Many tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_grounded, deep_research, and bet_research all querying data but with nuanced differences. The descriptions help but boundaries remain fuzzy, especially between ask_pipeworx and deep_research for broad vs. single lookups. Overall moderate ambiguity.

Naming Consistency2/5

Naming is inconsistent: some tools use snake_case (ask_pipeworx, ai_visibility_check), others use camelCase (serpapi_google_jobs), and patterns vary widely (e.g., pipeworx_feedback vs. compare_entities). Only the serpapi_google_* group follows a consistent pattern.

Tool Count3/5

36 tools is on the high side for a single server, with many meta-tools (discover_tools, suggest_questions) and niche prediction market tools. The scope seems overly broad, covering data lookup, prediction markets, memory, and subscriptions, which could be streamlined to a more focused set.

Completeness3/5

The server covers a wide range of domains (financial, economic, news, drugs, prediction markets, Google services), but lacks direct web search and write/update capabilities. While the coverage is broad, there are notable gaps (e.g., no generic web search, limited tool for modifying data) for a data-focused server.