Search Google Maps business listings by keyword and location. Returns name, category, address, phone, website, rating, review count, opening hours, and coordinates. No login, no Google Maps API key.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | What to search for (e.g. "dentists", "coffee shops") | |
| location | No | Where to search (e.g. "Austin, TX", "Bangalore") | |
| maxResults | No | Max listings (default 25) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description carries full burden. It discloses that no authentication is required ('No login, no Google Maps API key') and lists return fields. Missing details on rate limits or error handling, but adequate for a search tool.
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, no redundancy. First sentence states purpose and return types; second adds key usage note (no auth). Every sentence earns its place.
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 straightforward search tool with 3 parameters and no output schema, the description covers purpose, return fields, and a notable usage point. Lacks sorting or pagination details, but remains fairly complete.
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 baseline is 3. Description adds the context 'No login, no Google Maps API key' but does not enhance parameter meanings beyond the schema descriptions. For example, 'maxResults' default is mentioned but no additional guidance.
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?
Description clearly states the verb 'Search' and resource 'Google Maps business listings', listing specific return fields. Distinguishes from sibling tools like search_crunchbase and search_zillow, which target different domains.
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?
Description does not explicitly state when to use this tool versus alternatives, nor provides exclusion criteria. However, it implies usage for Google Maps business listings and notes 'No login, no Google Maps API key', which gives some context about ease of use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools target unique data sources or specific actions (e.g., search_zillow vs. get_zillow_property_details are clearly sequential). A few LinkedIn-related tools (find_linkedin_candidates vs. search_linkedin_employees) have overlapping purposes but their descriptions clarify distinct use cases. Overall, confusion is minimal and descriptions resolve ambiguity.
All tools follow a consistent verb_noun pattern in snake_case, using verbs like search, get, find, scrape, analyze, lookup, resolve, and verify. The pattern is predictable across the entire set, making it easy for an agent to infer function from name.
With 32 tools, the server exceeds the 'too many' threshold of 25+. While the broad scope of web data mining justifies some diversity, the count is unwieldy and could overwhelm an agent's selection process. A smaller, more focused set per domain would improve coherence.
The toolset covers a wide range of data retrieval needs: company research, real estate, job listings, academic research, and government records. For a read-only data aggregation service, there are no major lifecycle gaps, though some subdomains like social media scraping only cover Reddit and LinkedIn, missing other platforms. Overall, it is reasonably complete for its stated purpose.
