Google Scholar Remote MCP Server

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 transparency. It discloses that the tool returns citation snippets and export links, implying a read-only operation. However, it does not mention potential errors (e.g., invalid `resultId`), rate limits, or whether the operation is idempotent. This is a minor gap.

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

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

The description is concise and well-structured: a title line, a clear functional explanation, an output summary, and use-case guidance. No redundant information or fluff is present. Every sentence contributes to the tool's understanding.

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

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

Given that no output schema is provided, the description compensates by explicitly listing the expected output types (citation formats and export links). It also explains the input provenance (from google/scholar results) and the intended use cases, covering all essential context an agent needs to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with both parameters described. The `q` parameter is explained as the `resultId` from a google/scholar result, providing critical cross-tool context. The `hl` parameter includes guidance on format (two-letter code) and a range (159 allowed) with examples, which exceeds basic type information. These descriptions add meaningful value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: looking up citation formats and export links for a single Google Scholar result. It specifies the verb 'Looks up', identifies the resource type (Google Scholar organic result), and differentiates from sibling tools by referencing the `resultId` returned by the google/scholar endpoint. The use cases ('build citation/bibliography features') further clarify its purpose.

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

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

The description indicates when to use the tool: after obtaining a `resultId` from a google/scholar organic result, and for citation/bibliography needs. It implicitly contrasts with the search tool that produces the results. However, it does not explicitly mention alternative tools or scenarios where this tool would be inappropriate, but the context is sufficient for a clear use case.

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

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

The description states it 'scrapes Google Scholar' and 'returns' organic results, implying a read-only operation. However, there are no annotations and the description does not mention potential rate limits, blocking, authentication requirements, or the absence of side effects. It covers basic behavior but leaves some operational expectations implicit.

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

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

The description is a single focused paragraph that front-loads the purpose and then lists key features and use cases. It is not overly verbose, though it somewhat duplicates parameter details already present in the schema. Overall it is concise and well-organized.

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

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

The description compensates for the absence of an output schema by enumerating the result fields and linking them to parameter behavior. It includes enough context for an agent to understand expected outputs and typical applications. It lacks only explicit error/rate-limit handling, which is minor for a search GET endpoint.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

While the schema already describes all parameters (100% coverage), the prose description adds valuable context, such as examples for `asSdt` (e.g., '0,5' for Articles), the meaning of `scisbd` for date sorting, and how `cites`/`cluster` map to returned result fields. This goes beyond simple parameter names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as scraping Google Scholar for search results, listing the key query capabilities (author/source helpers, year range, citation lookups) and the exact output fields (title, link, snippet, cited-by, versions). This differentiates it from sibling tools like the citation formatter.

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

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

The description explicitly states recommended use cases ('academic research, literature review automation, citation tracking, and grounding research agents with scholarly sources'). It does not explicitly name alternative tools or provide 'when not to use' guidance, but the use cases are sufficiently specific to guide selection.

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