WebLens

search_scholar

Google Scholar academic search. Returns papers with snippets, publication info, and citation counts. Price: $0.005

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_scholar"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No	Maximum results to return
`query`	Yes	Search query

Schema Changelog

Changes observed during successful MCP inspections.

Changed9 schema fields changedOct 13, 2026
- addedInput schema / properties / limit / default
```
Added value: +10
```
- changedInput schema / properties / limit / description
```
Previous value: -"Number of results (default: 10, max: 20)"New value: +"Maximum results to return"
```
- addedInput schema / properties / limit / example
```
Added value: +10
```
- addedInput schema / properties / limit / maximum
```
Added value: +20
```
- addedInput schema / properties / limit / minimum
```
Added value: +1
```
- changedInput schema / properties / query / description
```
Previous value: -"Academic search query"New value: +"Search query"
```
- addedInput schema / properties / query / example
```
Added value: +"x402 payment protocol"
```
- addedInput schema / properties / query / maxLength
```
Added value: +500
```
- addedInput schema / properties / query / minLength
```
Added value: +1
```
First observedAug 4, 2026

TDQS

A3.6/5.0

Behavior3/5

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

With no annotations, the description carries the full burden and does disclose the return payload (snippets, publication info, citation counts) plus a per-call price of $0.005, which is genuinely useful cost context. It omits rate limits, auth requirements, and any note on result freshness or failure modes, so the behavioral picture is partial.

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?

Three short sentences, zero waste, with the core identity front-loaded and the return shape and cost following in order of importance. Nothing is padded or repeated.

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?

For a two-parameter read tool with a fully documented schema and no output schema, the description covers purpose, return contents, and cost, which is enough to call it correctly. The only real gap is explicit sibling routing guidance.

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% with only two simple parameters, so the schema already documents both query and limit fully. The description adds no syntax, quoting, or limit-tuning guidance beyond the schema, so baseline 3 applies.

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?

States a specific verb+resource ('Google Scholar academic search') and names the source corpus, which implicitly separates it from generic siblings like search_web. It does not explicitly contrast with deep_research or search_web, but the academic qualifier is enough to route correctly.

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 academic framing implies when to use it (scholarly literature lookups), but there is no explicit guidance on when to prefer it over search_web, deep_research, or answer_question, nor any exclusions. Usage must be inferred from the source name alone.

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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