Laspi Business Directory
Server Details
Search a directory of real, owner-confirmed small businesses. Read-only; attribution required.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.3/5 across 3 of 3 tools scored.
Each tool has a distinct purpose: ask_directory handles natural language needs, search_businesses handles keyword queries, and get_business retrieves detailed business cards by slug. There is no overlap or ambiguity between them.
Tool names follow a consistent verb_noun pattern (ask_directory, get_business, search_businesses), all in snake_case. This makes the tool set predictable and easy for an agent to understand.
With 3 tools, the server is well-scoped for a business directory. It provides search, natural language query, and detailed retrieval without unnecessary complexity. This count is appropriate for the domain.
The tool set covers the primary interactions needed: searching by keyword, asking a natural language question, and getting full details for a specific business. There are no obvious gaps for a directory of this nature.
Available Tools
3 toolsask_directoryAInspect
Ask the directory a customer question in natural language. Returns schema.org cards of businesses that genuinely answer it (LLM-reranked, strictly from the directory — never invented) + grounding links. Use when the customer describes a NEED rather than a search term.
| Name | Required | Description | Default |
|---|---|---|---|
| language | No | Optional service language filter (ISO code) | |
| question | Yes | Customer question |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description discloses key behaviors: LLM-reranking, strict adherence to directory data (never invented), and grounding links. It does not mention auth or rate limits, but these are less critical for a read-only query 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 efficiently convey purpose, output details, and usage guidance without any fluff. 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?
Despite no output schema, the description adequately describes the output nature (schema.org cards, LLM-reranked, grounding links) and usage scenario. It covers input and output sufficiently for a simple query 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%, baseline 3. The description adds value by framing the 'question' parameter as a natural language customer question for needs, and the 'language' filter is not elaborated further but the schema already provides ISO code info.
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 takes a customer question in natural language and returns schema.org cards of relevant businesses, distinguished from sibling tools by mentioning it's for needs versus search terms.
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 provides a usage context: 'Use when the customer describes a NEED rather than a search term,' which effectively differentiates it from search_businesses and implies when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_businessAInspect
Get the full public card of one business by its slug (from search results or a laspi.pro/b/{slug} URL): description, services, contacts, languages.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Business slug |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. 'Full public card' implies safe read operation. No side effects or restrictions mentioned, but adequate for a simple retrieval.
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?
Single sentence efficiently conveys purpose and parameter source. No wasted words; front-loaded with key 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?
Given low complexity, one required parameter, and no output schema, description lists return fields (description, services, contacts, languages) sufficiently. Sibling tools provided.
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 slug with description 'Business slug'. Description adds context that slug originates from search results or URLs, providing more meaning than schema alone.
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 verb 'Get' and resource 'full public card of one business' with specific identification method (slug). Lists included fields (description, services, contacts, languages), distinguishing it from siblings search_businesses and ask_directory.
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?
Implies use when slug is available from search results or URL, but does not explicitly mention when not to use or direct alternatives. Sibling context suggests usage after search_businesses, but not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_businessesAInspect
Search the Laspi public business directory (small businesses: services, location, languages). Returns top matches with name, description, city, languages and the canonical page URL. Use the customer's own words as the query, e.g. 'manicure in Alicante' or 'online QA course in Russian'.
| Name | Required | Description | Default |
|---|---|---|---|
| city | No | Optional city filter (substring) | |
| niche | No | Optional niche key filter | |
| query | Yes | Natural-language search query | |
| language | No | Optional service language filter (ISO code) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It states it returns top matches with fields but does not disclose ranking criteria, pagination, or potential limitations.
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 plus an example sentence, efficient and front-loaded with purpose. No unnecessary words.
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?
Covers purpose, return fields, and query formation. Lacks details on error handling or empty results, but sufficient for a search tool with no output 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?
Schema description coverage is 100%, so description adds minimal semantic value. It provides usage examples but does not add meaning beyond existing parameter descriptions.
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 tool searches a public business directory, returns top matches with specified fields, and gives example queries. It distinguishes from siblings (ask_directory, get_business) by focusing on search.
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 guidance to use the customer's own words for the query and gives concrete examples. However, does not explicitly state when not to use this tool or differentiate from siblings.
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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{
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