google-maps-mcp-server

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

Annotations already indicate readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds value by specifying the returned fields (company name, full address, contact phone, website, business category, review metrics) and the default yield ('20+ leads per query'). This goes beyond annotations, though it doesn't address potential limitations like data freshness or rate limits.

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 well-structured: it leads with the core action, lists the output fields, and concludes with practical use cases. Every sentence serves a purpose and there is no redundancy or 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 moderately complex tool with four parameters, no output schema, and existing annotations, the description is fairly complete. It covers the purpose, output, and use cases. It could enhance completeness by noting data source caveats or how results scale, but the current details (return fields, default 20+) provide a solid understanding.

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 fully documents all four parameters. The description merely summarizes 'business category and geography', adding little beyond the schema. The baseline for full coverage is 3, and the description doesn't elevate it with additional details or nuances.

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: extract B2B lead lists from Google Maps by business category and geography. It specifies the resource (Google Maps), the output (company name, address, phone, website, etc.), and the context (sales prospecting, market research, vendor lists), effectively distinguishing it from siblings like search_local_businesses and validate_emails.

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 names use cases: 'Use for sales prospecting, market research, or building vendor lists.' This provides clear context for when to use the tool, though it does not explicitly mention alternatives or exclusions. The sibling tools exist, but the description doesn't say when to prefer one over the other, so it falls short of a full 5.

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 declare readOnlyHint=true and openWorldHint=true, so safety profile is already clear. The description adds value by detailing the returned fields (business name, address, rating, review count, phone, website, category, open/closed status), which is useful context beyond the 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?

Three concise sentences: purpose, return data, and use cases. No redundant wording or filler; information is front-loaded and every sentence contributes.

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 covers purpose, return data, and use cases, which is suitable for a search tool with 3 params and no output schema. Minor gap: location is optional in the schema but the description implies both query and location are needed, and max_results behavior isn't discussed but is in the schema.

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 input schema has 100% description coverage, with all three parameters fully documented with examples and defaults. The tool description does not add additional parameter-level information beyond what the schema already provides, so 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 clearly states 'Search Google Maps for local businesses matching a query and location', identifying the exact verb and resource. It also enumerates the return data, distinguishing it from sibling tools like generate_leads and validate_emails.

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?

Explicit use cases are provided: 'restaurant discovery, service provider lookup, or competitive local analysis'. This gives clear context on when to use the tool, though it does not explicitly mention alternatives or exclusion criteria.

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 (readOnlyHint: true, openWorldHint: true) already cover the safety profile, and the description adds concrete behavioral context by listing what the tool checks (syntax, domain validity, SMTP verification) and what it returns (pass/fail status, errors, verification result). This goes beyond the annotations without contradicting them.

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 sentences, front-loaded with the action and resource, followed by return summary and usage note. Every clause earns its place; no filler or 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?

With no output schema, the description compensates by summarizing the return fields (pass/fail, syntax errors, domain validity, SMTP result) and the intended use case. Complexity is low (single array parameter), and the annotations cover safety. Missing minor details like rate limits or batch size, but the tool is sufficiently specified.

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%: the only parameter 'emails' is fully described as an array of email strings with examples. The description reinforces that validation happens 'per email' but does not add new syntax or format details beyond what the schema already states. Per the rubric, the baseline is 3 when schema coverage is high.

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 opens with a specific verb ('Validate and verify') and resource ('email addresses'), then details the scope: 'deliverability, format compliance, and mailbox existence.' This clearly distinguishes it from sibling tools (generate_leads, search_local_businesses) which focus on finding prospects, not validating existing addresses.

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 when to use: 'Use before sending bulk emails to prevent bounces and protect sender reputation.' It gives a clear context but does not explicitly mention alternatives or when not to use it, such as for lead generation or address discovery.

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