Local Business Data MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation3/5
Most tools have distinct purposes, but there is notable overlap between search, bulk_search, search_in_area, and search_nearby, which all perform business searches with slight variations. Additionally, business_reviews and business_reviews_v2 appear redundant, though v2 might offer improved pagination. The descriptions help differentiate them, but an agent could still misselect among the search variants.
Naming Consistency4/5
The naming follows a consistent snake_case pattern throughout, with clear verb_noun structures like business_details and reverse_geocoding. However, there are minor deviations: autocomplete lacks a verb prefix, and business_reviews_v2 introduces a version suffix that breaks the pure verb_noun convention. Overall, the naming is mostly predictable and readable.
Tool Count5/5
With 13 tools, the count is well-scoped for a local business data server, covering various aspects like searching, details, photos, reviews, and geocoding. Each tool earns its place by addressing specific needs in the domain, such as batch processing or different search methods, without feeling excessive or thin.
Completeness4/5
The toolset provides comprehensive coverage for local business data, including search, details, photos, reviews, and geocoding, with support for batching and pagination. Minor gaps exist, such as no explicit tools for updating or deleting data (though this may be intentional for a read-only API), and the redundancy between business_reviews and business_reviews_v2 could be streamlined. Overall, it supports core workflows effectively.
Average 3/5 across 13 of 13 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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}
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'paginate' which hints at pagination behavior, but doesn't explain what 'Business Owner Posts' actually are, what format they return in, whether there are rate limits, authentication requirements, or any other operational characteristics. The incomplete ending further reduces clarity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is technically brief but suffers from poor structure and an incomplete ending with a stray quote mark. While it's only one sentence, the abrupt cutoff makes it feel unfinished rather than concise. Every sentence should earn its place, but this one doesn't feel properly completed.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 4 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what 'Business Owner Posts' are, what the output looks like, or provide any context about the data source or limitations. The incomplete ending further reduces completeness. With rich sibling tools available, more context is needed to understand this tool's specific role.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents all four parameters. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it doesn't explain how parameters interact or provide usage examples. With complete schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get all / paginate') and resource ('Business Owner Posts'), making the purpose understandable. However, it doesn't differentiate this tool from potential siblings like 'business_details' or 'business_reviews' that might also retrieve business-related information, and the description ends abruptly with an incomplete quote mark.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'business_details', 'business_reviews', and 'business_photos' available, there's no indication whether this tool is for posts specifically, how it differs from other business data retrieval tools, or any prerequisites for use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the geographic area definition but doesn't cover important aspects like rate limits, authentication requirements, pagination behavior, error conditions, or what happens when no results are found. The Google Maps reference is confusing rather than clarifying.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The first sentence is clear and front-loaded, but the second sentence about Google Maps is confusing and doesn't add value for an AI agent. The description could be more concise by removing the instructional content and focusing purely on tool functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex tool with 11 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns, how results are structured, error handling, or performance characteristics. The Google Maps reference creates confusion rather than providing helpful context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 11 parameters thoroughly. The description adds minimal value beyond what's in the schema - it mentions center coordinate and zoom level (which are already well-described in the schema) but doesn't provide additional context about parameter interactions or usage patterns.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches for businesses in a specific geographic area defined by coordinates and zoom level. It specifies the resource (businesses) and geographic scope, but doesn't explicitly differentiate from sibling tools like 'search' or 'search_nearby' beyond mentioning the area definition.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'search', 'search_nearby', or 'bulk_search'. The second sentence about Google Maps is instructional rather than providing usage context. There's no mention of prerequisites, limitations, or comparative advantages.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. While it mentions the core action (searching businesses near coordinates), it lacks critical behavioral details: whether this is a read-only operation, what authentication might be required, rate limits, pagination behavior, error conditions, or what the response format looks like. The Google Maps reference adds some context but doesn't cover operational traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences: the first states the purpose clearly, but the second is an incomplete fragment ('To see it in action, right click on a specific point in the map on Google Maps and select "') that adds little value and ends abruptly. While the first sentence is efficient, the second is wasteful and poorly structured, reducing overall effectiveness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (10 parameters, no output schema, no annotations), the description is inadequate. It doesn't explain what the tool returns, how results are structured, or any behavioral constraints. For a search tool with many parameters and no structured output documentation, users need more context about response format, error handling, and operational limits to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 thoroughly with descriptions, defaults, and examples. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions geographic coordinates generally but doesn't provide additional syntax, format, or usage details for parameters. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search businesses near by specific geographic coordinates.' It specifies the verb ('search'), resource ('businesses'), and scope ('near by specific geographic coordinates'). However, it doesn't explicitly differentiate from sibling tools like 'search' or 'search_in_area', which likely have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'search' or 'search_in_area'. The second sentence about 'right click on a specific point in the map on Google Maps' is a usage example rather than contextual guidance. There's no mention of prerequisites, limitations, or comparative advantages.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions batching support, which is useful, but lacks critical details such as whether this is a read-only operation, potential rate limits, authentication requirements, error handling, or what the response format looks like. For a tool with 7 parameters and no output schema, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and front-loaded, consisting of two sentences that directly state the tool's purpose and a key feature (batching). There is no wasted language, and every sentence earns its place by providing essential information efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (7 parameters, no annotations, no output schema), the description is incomplete. It adequately states the purpose but fails to provide necessary behavioral context, usage guidelines, or details on output. For a tool that likely returns structured business data, the lack of information on response format or error handling makes it insufficient for an agent to use effectively without guesswork.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, meaning all parameters are documented in the input schema. The description adds minimal value beyond the schema by implying that 'business details' include 'emails and social contacts,' which loosely relates to the 'extract_emails_and_contacts' parameter. However, it doesn't provide additional semantics, syntax, or format details for any parameters, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get full business details including emails and social contacts.' It specifies the verb ('Get'), resource ('business details'), and scope ('including emails and social contacts'), which is specific and actionable. However, it doesn't explicitly differentiate this tool from sibling tools like 'search' or 'business_reviews', which might also retrieve business information but with different focuses.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal usage guidance. It mentions 'Supports batching of up to 20 Business Ids,' which hints at a use case for multiple IDs, but it doesn't explain when to use this tool versus alternatives like 'search' or 'business_reviews'. There's no mention of prerequisites, exclusions, or specific scenarios where this tool is preferred, leaving the agent with little contextual direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While 'Get' implies a read operation, the description doesn't mention important behavioral aspects like authentication requirements, rate limits, pagination behavior (beyond the cursor parameter), error conditions, or what format/type of photos are returned. This leaves significant gaps for an agent to understand how to properly use this tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just 5 words, front-loading the core purpose without any unnecessary elaboration. Every word serves a clear purpose: verb (Get), resource (business photos), and key identifier (by Business Id). There's zero waste or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 6 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't address what the tool returns (photo objects, URLs, metadata), doesn't explain the relationship between parameters like 'cursor' and 'limit' for pagination, and provides no context about error handling or typical use cases. The agent would struggle to use this tool effectively based solely on this description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so all parameters are well-documented in the schema itself. The description adds no additional parameter information beyond what's already in the schema, maintaining the baseline score of 3. The description doesn't explain parameter interactions or provide usage examples that would add value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and resource ('business photos') with a specific identifier ('by Business Id'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'business_photo_details' or 'business_details', which likely provide related but different functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'business_photo_details' and 'business_details' available, there's no indication of when this photo retrieval tool is appropriate versus those other tools that might provide more detailed or different photo-related information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'pagination support' but doesn't explain how pagination works (e.g., using limit/offset), rate limits, authentication needs, error handling, or what the response looks like. For a tool with 9 parameters and no output schema, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core functionality ('Get business reviews by Business Id') and includes a key feature ('with pagination support'). There's no wasted verbiage or redundancy, making it highly concise and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (9 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral aspects like response format, error cases, or usage context relative to siblings. While concise, it doesn't provide enough information for an agent to confidently invoke the tool without relying heavily on the schema alone.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 9 parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't clarify parameter interactions or provide usage examples). With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get business reviews by Business Id with pagination support.' It specifies the verb ('Get'), resource ('business reviews'), and key constraint ('by Business Id'). However, it doesn't differentiate from sibling tools like 'business_review_details' or 'business_reviews_v2', which is a minor gap.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'business_review_details' and 'business_reviews_v2' available, there's no indication of how this tool differs or when it should be preferred, leaving the agent to guess based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that this is an 'endpoint' but doesn't describe key behavioral traits such as rate limits, authentication requirements, error handling, or response format. The description is minimal and lacks operational context beyond the basic purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that clearly states the purpose upfront. However, it ends abruptly with 'This endpoint implements the \"', which appears incomplete or truncated, slightly reducing its effectiveness. Otherwise, it's appropriately sized with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for a tool with 5 parameters and no behavioral context. It lacks information on response format, error conditions, and operational constraints. While the purpose is clear, the description doesn't compensate for the missing structured data, leaving significant gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 5 parameters with descriptions and defaults. The description adds no parameter-specific information beyond implying latitude and longitude are used for reverse geocoding. This meets the baseline of 3 since the schema does the heavy lifting, but the description doesn't enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get the details of a place or address in a specific geographic location by latitude and longitude (reverse geocoding).' It specifies the verb ('Get'), resource ('details of a place or address'), and method ('by latitude and longitude'). However, it doesn't explicitly differentiate from sibling tools like 'search' or 'search_nearby' which might also involve location-based queries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'search' or 'search_nearby' that might be relevant for location-based queries, nor does it specify use cases or exclusions. The agent must infer usage from the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 disclosure. While it mentions the email/social profile extraction feature, it doesn't address important behavioral aspects like rate limits, authentication requirements, data freshness, whether this is a read-only operation, or what format the results come in. The description is insufficient for a tool with 13 parameters and no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states the core functionality upfront. It's appropriately sized for the tool's purpose, though it could potentially be more structured with clearer separation between primary search functionality and the email extraction feature.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex tool with 13 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns, how results are structured, whether there are pagination considerations, or what happens when the email extraction feature is enabled. The description should provide more context about the tool's behavior and output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the schema already documents all 13 parameters thoroughly. The description adds minimal value beyond what's in the schema - it only hints at the 'extract_emails_and_contacts' parameter's purpose. This meets the baseline expectation when schema coverage is high.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches for local businesses on Google Maps and mentions the optional email/social profile extraction feature. It specifies the resource (local businesses) and platform (Google Maps), though it doesn't explicitly differentiate from sibling tools like 'search_in_area' or 'search_nearby' which might have similar functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'search_in_area', 'search_nearby', or 'bulk_search'. It mentions the email extraction feature but doesn't explain when this should be enabled or what alternatives exist for similar functionality.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns predictions but does not describe key behaviors like response format, pagination, rate limits, authentication needs, or error handling. For a tool with no annotation coverage, this is a significant gap, as the agent lacks essential operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core functionality without unnecessary words. It directly states what the tool does, making it easy to parse and understand quickly. There is no wasted verbiage, and it effectively communicates the purpose in a compact form.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (4 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavior, usage guidelines, and output structure. Without annotations or an output schema, the agent must infer missing information, making the description incomplete for reliable tool invocation in more complex scenarios.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, meaning the input schema fully documents all parameters. The description does not add any semantic details beyond what the schema provides, such as explaining how parameters interact or providing usage examples. Since the schema handles the heavy lifting, a baseline score of 3 is appropriate, as the description neither compensates for gaps nor adds extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool 'returns place/address, business and query predictions for text-based geographic queries,' which specifies the verb (returns), resources (place/address, business, query predictions), and scope (text-based geographic queries). However, it does not explicitly differentiate from sibling tools like 'search' or 'bulk_search,' which may also handle geographic queries, leaving some ambiguity about when to choose this tool over others.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions the purpose but does not specify scenarios, prerequisites, or exclusions, such as comparing it to 'search' or 'bulk_search' for similar geographic queries. This lack of context makes it challenging for an AI agent to select the appropriate tool without additional inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 disclosure. It mentions batching support for up to 20 author IDs, which adds useful context beyond the schema. However, it lacks details on permissions, rate limits, error handling, or response format, leaving significant gaps for a tool that likely involves external API calls.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. It directly states what the tool does and the key parameters, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is moderately complete for a read operation. It specifies the tool's purpose and parameters, but lacks details on return values, error cases, or integration context, which could hinder an agent's ability to use it effectively without trial and error.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 parameters thoroughly. The description adds minimal value by reiterating the business_id and review_author_id parameters without providing additional semantics or examples beyond what's in the schema, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get the details') and resource ('specific review'), specifying it retrieves details for a review identified by business and author IDs. It distinguishes from siblings like 'business_reviews' (which likely lists reviews) by focusing on a single review's details, but doesn't explicitly contrast with 'business_reviews_v2'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'business_reviews' or 'business_reviews_v2'. The description implies it's for fetching details of a specific review, but lacks explicit context about prerequisites, when not to use it, or comparisons with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states basic functionality. It doesn't disclose behavioral traits such as rate limits, authentication requirements, error handling, response format, or whether it's read-only/destructive. The mention of 'pagination support' is useful but insufficient for a tool with 9 parameters and no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core purpose and includes an important feature (pagination support). Every word earns its place without redundancy or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (9 parameters, no output schema, no annotations), the description is incomplete. It lacks details on response format, error conditions, usage constraints, and how parameters like 'translate_reviews' or 'fields' affect behavior. For a data retrieval tool with rich filtering options, more contextual guidance is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 9 parameters. The description adds no parameter-specific information beyond implying 'business_id' is key and mentioning pagination (related to 'cursor'). Baseline 3 is appropriate as the schema does the heavy lifting, but the description doesn't enhance understanding of parameter interactions or semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and resource 'business reviews', specifying the primary identifier 'by Business Id' and mentioning 'pagination support'. It distinguishes from siblings like 'business_review_details' (single review) and 'business_reviews' (likely older version), but doesn't explicitly contrast with all alternatives like 'search' for broader queries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving reviews for a specific business, but provides no explicit guidance on when to use this tool versus alternatives like 'search' or 'business_review_details'. It mentions pagination support, which hints at use for large datasets, but lacks clear when/when-not instructions or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions batching support (up to 20 queries), which is useful context. However, it lacks critical details: it doesn't specify rate limits, authentication needs, error handling, or what the response looks like (no output schema). For a search tool with no annotation coverage, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: two sentences with zero waste. The first sentence states the core purpose, and the second adds crucial behavioral context (batching). Every sentence earns its place by providing distinct, valuable information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 1 parameter with full schema coverage and no output schema, the description is moderately complete. It covers the tool's purpose and batching behavior but lacks details on response format, error conditions, or performance constraints. For a search tool with batching, more context on output structure or limitations would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 the single parameter 'X-User-Agent' with its type and default. The description doesn't add any parameter-specific information beyond what's in the schema. Baseline 3 is appropriate when the schema does the heavy lifting, though the description could have explained parameter relevance to batching.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search local businesses on Google Maps' specifies the action (search) and resource (local businesses on Google Maps). It distinguishes from siblings like 'search' (generic) and 'search_in_area' (geographically constrained) by emphasizing bulk/batching capability. However, it doesn't explicitly contrast with all siblings like 'business_details' or 'reverse_geocoding'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: 'Batching of up to 20 queries is supported in a single request' implies it's optimal for multiple searches at once. It doesn't explicitly state when NOT to use it or name alternatives (e.g., use 'search' for single queries), but the batching context strongly guides usage decisions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 disclosure. It effectively describes the batching capability (up to 20 Photo IDs) and the type of details returned, which is useful. However, it lacks information on rate limits, authentication requirements, error handling, or response format, leaving gaps in operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence and efficiently adds batching details in the second. Every sentence contributes essential information without redundancy, making it appropriately sized and well-structured for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 required parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the purpose and batching behavior but lacks details on response structure, error cases, or operational constraints, which are important for effective tool invocation in the absence of structured metadata.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema fully documents both parameters. The description adds minimal value beyond the schema by mentioning batching support, which is already covered in the 'photo_id' schema description. No additional semantic context is provided, aligning with the baseline score for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get extra details') and resource ('about a business photo'), specifying the exact information returned (caption, owner name and avatar, and more information). It distinguishes itself from sibling tools like 'business_photos' (which likely lists photos) and 'business_details' (which covers general business info) by focusing on photo-specific metadata.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool—when needing detailed metadata for business photos, including batching support. However, it does not explicitly state when not to use it or name alternatives among siblings (e.g., 'business_photos' for basic photo lists), leaving some ambiguity in tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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