Google Scholar Remote MCP Server

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

Annotations already declare it is a safe read (readOnlyHint=true, destructiveHint=false), lowering the bar. The description still adds useful context: it operates on a single result, and enumerates the concrete outputs (MLA/APA/Chicago/Harvard/Vancouver plus BibTeX/EndNote/RefMan/RefWorks), which is behavior an agent cannot infer from annotations.

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?

Two tight paragraphs, front-loaded with the core action and followed by dependency and output detail. Every sentence carries operational value; nothing is padding.

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?

For a read-only single-result lookup with no output schema, the description compensates by enumerating the returned citation formats and export links. Combined with annotations and full schema coverage, an agent has everything needed to invoke it 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 description coverage is 100%, so both parameters (q, hl) are fully documented in the schema, establishing the baseline of 3. The description restates that resultId comes from google/scholar, echoing rather than extending the schema text, so no uplift is warranted.

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?

States a specific verb+resource ('Looks up citation formats and export links') scoped to a single Google Scholar organic result identified by resultId. This clearly distinguishes it from the sibling google_scholar_scholar_getScholarSearchResults, which produces the resultId this tool consumes.

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?

Gives clear when-to-use guidance ('build citation/bibliography features or complete a research workflow started with google/scholar') and names the upstream dependency that produces the required resultId. It stops short of explicitly naming the sibling alternative, but the dependency routing is enough to select it correctly.

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?

Annotations already declare readOnlyHint, destructiveHint=false and openWorldHint, so safety is covered. The description adds real value beyond them by disclosing the return shape (organic results with cited-by/related/versions links, caseHasdataLink for case-law results) and the asSdt court-code semantics, which annotations cannot convey.

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?

Core purpose is front-loaded in the opening clause, followed by parameter coverage and return/use-case detail. The first sentence is a dense run-on enumerating many parameters, but each clause carries information rather than filler.

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?

For a 15-parameter tool with no output schema, the description compensates by describing the result fields and the case-law link behavior, and annotations cover the safety profile. Coverage is strong; only the absence of an explicit alternative-selection rule against the cite sibling leaves a minor gap.

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%, so the baseline is 3, but the description goes further by explaining parameter intent: cites/cluster as lookups keyed off returned IDs, asSdt court-code values (2006 all courts, 2003 Federal), review-article and citation-inclusion filters, and scisbd sort modes. This adds meaning beyond the schema text.

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?

States a specific verb (scrapes) and resource (Google Scholar search results) and immediately distinguishes the tool from its scholar siblings by calling out cited-by/all-versions lookups and routing case-law results onward. An agent can identify this as the general Scholar search entry point without opening the schema.

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?

Explicitly lists when to use it (academic research, literature review automation, citation tracking, grounding research agents) and points to the sibling google/scholar-case-law flow for case law. It does not state when NOT to use it or contrast against hasdata_google_scholar_cite, but the context is clear.

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