Google Search Remote MCP Server

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

No annotations provided, but the description clearly explains the output and the continuation mechanism, making behavior transparent.

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 detailed and informative, though slightly verbose with repeated examples; it is still well-structured and front-loaded.

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 fully explains what the tool does, its output, and its multi-turn continuation feature, making it complete for the given complexity.

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 covers all parameters with descriptions; the tool description does not add additional meaning beyond the schema, so a neutral score.

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?

Clearly states it gets AI Mode SERP results from Google, distinguishing from other Google SERP tools like AI Overview.

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?

Provides explicit use cases and examples, but does not explicitly compare with alternative tools, so slightly less than perfect.

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?

With no annotations, the description carries the full burden and does well: it discloses the lazy-loaded nature, the 1-minute pageToken validity window, and the shape of the payload (answer text, referenced source URLs, expanded subtopics). It does not cover error behavior for expired tokens or auth requirements, leaving a modest 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?

Three tight sentences: capability, mechanism, then use cases. The leading 'Get AI Overview Results' line largely restates the title, which is mild redundancy, but nothing else is wasted and the key constraint is front-loaded.

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?

No output schema exists, so the description correctly compensates by enumerating the return components (answer text, source URLs, subtopic sections) and the required token. It omits edge-case handling such as expired-token errors, which is the main remaining gap for a follow-up-only tool.

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% for the single pageToken parameter, so the schema already documents its origin and 1-minute validity. The description restates the same facts without adding format or syntax detail beyond the schema, making 3 the correct baseline.

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 and resource — fetching the lazy-loaded Google AI Overview block via a pageToken returned by the Google SERP API — and scopes it clearly against the broader SERP siblings by framing it as a follow-up call rather than a primary search. An agent can tell this apart from ai_mode and serp tools without opening a 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 frames the when: a follow-up call after Google SERP, for SEO AI-citation tracking, fact-checking answers against sources, and LLM retrieval pipelines. It stops short of naming an alternative sibling (e.g. ai_mode) or stating when not to use it, so no exclusion guidance is given.

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?

With no annotations, the description carries the full burden and does well: it discloses the response payload (venue fields, map link, ticket sources with official/unofficial flags) and a critical timing constraint (pageToken expires in minutes). It omits auth/rate-limit details, but the behavioral profile is largely conveyed.

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?

Front-loads the purpose, then layers in token provenance, return contents, and the expiry warning. Slightly dense, but every sentence contributes actionable information and none is 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?

With no output schema, the description appropriately enumerates the returned fields so the agent knows what to expect. Combined with parameter sourcing and the expiry warning, an agent has enough to call the tool correctly; only auth/prerequisite context is missing.

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 already 100%, so the baseline is 3. The description adds value beyond the schema by naming two sources for the value (the `eventsResults` array field or the ready-to-call `hasdataLink`) and reiterating the expiry, which helps the agent obtain a valid token.

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 ('looks up') and resource ('a single Google event'), and clearly positions itself as the detail-enrichment step for SERP events. It is distinguishable from sibling SERP search tools because it operates on a single event via a pageToken rather than returning result lists.

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 a clear context ('use to enrich an events search result') and an operational constraint ('fetch it and call this endpoint promptly' due to token expiry). It implies the preceding google/serp call but never names an alternative sibling or a when-not-to-use condition, so it stops short of full routing guidance.

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?

With no annotations, the description carries the full burden. It describes the read-like behavior (expands, returns) and mentions pagination and optional store expansion, but it does not explicitly state that it is a read-only operation or disclose any potential side effects, auth requirements, or rate limits. The description is informative but not fully explicit.

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, with the core purpose front-loaded. It includes a useful list of return fields and use cases without excessive verbosity.

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 there is no output schema, the description adequately explains what is returned (offers, specs, images, ratings, nextPageToken). It also covers key parameters and use cases. It does not mention error handling or token lifecycle, but these are not critical for a simple read tool.

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 schema already documents all parameters. The description adds minor context (e.g., moreStores expands from 3–5 to up to 13, nextPageToken paginates) but largely repeats what the schema says. It adds value but not significantly 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 states a clear verb and resource: it expands the Google Shopping Immersive Product pop-up given a token. It distinguishes itself by focusing on the immersive product view, but it does not explicitly name sibling tools or contrast with them, so it misses the top score.

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?

It provides clear use cases (price-comparison bots, merchant discovery, dropshipping research) but does not explicitly state when to avoid it or name alternatives. The context is clear but exclusions are absent.

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?

With no annotations provided, the description carries the burden of behavioral disclosure. It transparently describes the return payload (title, snippet, source publisher, published date, thumbnail, URL) and the navigation tokens, and notes that storyToken supports sorting by relevance/date. It does not mention limits, errors, or rate restrictions, but for a read-oriented GET endpoint the description provides solid behavioral context.

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 compact, front-loaded with the core purpose, and every sentence adds useful information about retrieval modes, output, and use cases. It is dense but not bloated, and it avoids unnecessary repetition of schema details.

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 the 8-parameter schema with full descriptions and no output schema, the description adequately covers what the tool returns, the main input modes, and appropriate usage scenarios. It does not cover pagination or result limits, but the schema and stated output fields give an agent enough to invoke and interpret 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 description coverage is 100% and every parameter has a meaningful description with mutual-exclusion constraints. The tool description adds value by grouping the parameter types and explaining the story-cluster sort, but it mostly paraphrases what the schema already documents. Baseline 3 is appropriate since the schema does the heavy lifting.

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 a specific verb ('Retrieves Google News results') and resource, listing the distinct retrieval modes (free-text query, topicToken, sectionToken, publicationToken, storyToken) and the returned fields. It distinguishes itself from the general google_serp_serp_getSearchResults sibling by focusing specifically on Google News and its token-based navigation.

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 enumerates intended use cases: 'news monitoring, brand/PR tracking, topical aggregators, publisher-specific feeds, and drilling into full story coverage.' It does not explicitly name a sibling as an alternative or state when not to use this tool, but the use-case framing gives an agent clear selection context.

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?

No annotations are provided, so the description carries the full burden. It clearly frames the operation as read-only ('Pulls') and discloses searchType-dependent return content: title, images, price, ratings, specs, merchant offers, and review text. It does not cover rate limits, auth, or errors, but the core behavioral contract is present.

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 compact and front-loaded: a one-line summary, then the detailed behavior, then use cases. Every sentence contributes value, and there is no meaningful fluff.

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 9-parameter tool with no output schema, the description provides a strong high-level output contract and common use cases, while the schema covers parameter details. The main gap is that optional searchType has no documented default behavior, and pagination guidance relies entirely on the schema rather than the description.

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%, and the schema already explains filters, pagination, enums, and parameter constraints. The description paraphrases searchType and filters but adds no new parameter-level semantics, so the baseline of 3 applies.

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 states a specific verb and resource: 'Pulls detailed product data from Google Shopping by productId' with searchType variants and filters. This clearly distinguishes it from sibling search tools like shopping_getSearchResults, which are search-oriented rather than product-ID lookups.

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 lists task-based use cases: price intelligence, catalog enrichment, review mining, competitor spec comparison, and building shopping assistants. It does not name exclusions or alternative sibling tools, but it gives clear context for when this tool is appropriate.

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?

With no annotations, the description carries the behavioral disclosure burden. It does disclose that this is a scraper, enumerates filtering capabilities, and lists returned elements including organic results, ads, knowledge graph, People Also Ask, local pack, and AI Overview pageToken. However, it does not mention operational caveats such as rate limits, authentication needs, blocking risks, or pagination limits beyond offset/num.

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 front-loaded with a one-line summary and then organized into capability, return-value, and use-case sections. It is long, but the length is justified by the tool's 19 parameters and rich feature set; the examples are concrete 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 complex 19-parameter tool with no annotations and no output schema, the description is fairly complete: it explains what the tool returns and gives practical invocation examples. It does not discuss alternatives or limitations, but an agent has enough information to select and call 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 description coverage is 100%, so all 19 parameters are already documented in the input schema. The description only groups them into capability families like location/uule, country, language, tbm, and offset/num pagination, adding no parameter-level meaning beyond what the schema already provides.

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 names the operation ('Get Google Search Results') and identifies the tool as a 'full-featured Google Search scraper', listing parameter families and returned SERP components. It clearly conveys that this is the generic Google web-search endpoint rather than an image, news, or shopping variant, though it does not explicitly distinguish itself from the sibling serp_light tool.

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 gives concrete use cases: SEO rank tracking, keyword research, SERP-feature monitoring, competitor analysis, grounding LLMs, and person/company data enrichment with example queries. It does not explicitly state when not to use it or when to prefer serp_light, google_images, or AI-overview siblings, so it stops short of full exclusion guidance.

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?

No annotations are provided, so the description carries the burden. It accurately describes behavior by listing what is returned and what is not served, and the GET / title implies a read-only operation. It does not mention potential side effects, but none are expected for a search scraper.

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 relatively long but each sentence adds value by enumerating supported and unsupported result types and listing intended use cases. It is well-structured and front-loads the core purpose.

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 there is no output schema, the description compensates by summarizing the response components and limitations. It also provides enough context for an agent to decide when to use this tool versus the full SERP sibling tool.

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?

The schema already provides 100% coverage with detailed descriptions for all 12 parameters, so the description adds little beyond the schema. It does not introduce new parameter-specific meaning, though it provides useful context for how some result filters affect output.

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?

Clearly states it gets Google Search results from the light no-JS layout and explicitly lists the result types returned and excluded. The name and title also reinforce the specific resource and operation.

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 distinguishes this light version from the full SERP API by noting it is cheaper and faster, and names concrete use cases: high-volume keyword monitoring, bulk rank tracking, backlink discovery, and AI Overview presence tracking.

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?

With no annotations provided, the description carries the full responsibility for disclosing behavioral traits. It does not explicitly state whether the operation is read-only or if there are side effects, rate limits, or permissions. The phrasing 'Scrapes Google Shopping listings' implies a passive retrieval, but it lacks an explicit statement about non-destructiveness or data handling.

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, with a clear opening statement, a summary of output, and a list of use cases. It avoids redundancy and stays focused, making it easy for users to quickly grasp the tool's function and applications. The structure is efficient with no unnecessary information.

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?

Despite lacking an output schema, the description compensates by enumerating the expected return fields (e.g., product title, price, merchant) and explaining how to apply filters via 'hasdata_link'. It also covers parameter interactions for shoprs and pagination. Given the tool's moderate complexity, the description provides a complete picture for invocation without leaving critical gaps.

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?

The schema already provides detailed descriptions for all 10 parameters, covering their semantics thoroughly. The tool description does not add significant extra meaning beyond summarizing the overall purpose and mentioning a few output fields. Since schema coverage is 100%, the baseline score of 3 is appropriate, as the description adds marginal value.

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 that the tool scrapes Google Shopping listings for a search query and returns detailed product information. It explicitly mentions the output fields, such as product title, price, and merchant, making the tool's purpose unambiguous. The name 'Get Shopping Search Results' further reinforces its function.

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 provides specific use cases like e-commerce price tracking, catalog building, and promotion discovery, which guide when to employ the tool. It also hints at integration with other tools by mentioning feeding product IDs into the Product API or tokens into the Immersive Product API. However, it does not explicitly contrast it with sibling tools, leaving a small gap in direct alternative selection.

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?

No annotations exist, so the description carries the full burden and does well: it discloses page-based pagination (0-indexed), tbs filter support, per-platform provenance fields, and that it returns ready-to-call HasData API links for comments/transcript/profile. It omits auth requirements, rate limits, and failure modes, so not a full 5.

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?

Front-loaded with the core action and target, then scoped parameters, then return values, then use cases. The second sentence is dense but every clause (filters, return fields, hasdataLinks) adds distinct information, so it earns its length despite being long.

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 10-parameter scraping tool with no output schema and no annotations, the description compensates well by enumerating the returned fields and the linked follow-up endpoints. It lacks any note on authentication or result limits, which would complete the picture.

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 the schema already documents all 10 parameters in detail (including enums and examples). The description only lists parameter families (location/uule, gl/cr, hl/lr, deviceType, page) without adding format or constraint detail beyond the schema, so the baseline 3 applies.

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 names a specific verb ('Scrapes') and a precise resource ('the Google Short Videos carousel') and enumerates the covered platforms (TikTok, YouTube Shorts, Instagram Reels). This clearly distinguishes it from siblings like youtube_search_getYoutubeSearchResults or tiktok_search_getTikTokSearch, which target single platforms.

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?

It provides concrete usage contexts ('short-form content discovery, viral-trend monitoring, influencer research, cross-platform video aggregation, sourcing short clips to summarize or embed in LLM responses'). However it never states when NOT to use it or names an alternative tool (e.g., a single-platform search) for narrower needs.

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