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LeadIntel

LeadIntel

by rushikesheleven
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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get_business_details

Retrieve enriched business details by ID from available data providers and optionally discover a missing official website to support B2B lead qualification.

Instructions

    Retrieve enriched details for a specific business by ID.

    Looks up the entity across all available providers (OpenStreetMap, demo fixtures).
    If the business is missing a website and `auto_enrich_website` is True,
    attempts to discover its official website.

    Args:
        business_id: Unique identifier (e.g. 'osm_node_12345', 'osm_way_67890', or 'demo_001').
        auto_enrich_website: If True, search for missing official website if not present in OSM tags.

    Returns:
        Dict containing:
          - found (bool): Whether the business was resolved
          - business (dict|null): Enriched business record
          - provider (str): Source of the data
          - attribution (str|null): Mandatory attribution notice if source is OSM
          - enriched_fields (list[str]): Fields added or updated via enrichment

    Compliance:
        - Respects OSM ODbL attribution.
        - Does not scrape private personal information.

Input Schema

NameRequiredDescriptionDefault
business_idYes
auto_enrich_websiteNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does substantial work: it discloses multi-provider resolution (OSM, demo fixtures), the conditional enrichment side behavior, the ODbL attribution obligation, and a privacy constraint against scraping PII. It does not say whether enrichment results are persisted or whether the enrichment call adds latency, which are the remaining behavioral gaps.

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?

Purpose is front-loaded in the first sentence, followed by behavior, args, returns, and compliance in a predictable structure. The Returns and Compliance blocks are justified because no output schema or annotations exist, though the prose is slightly padded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-parameter read tool with no annotations and no output schema, the description covers purpose, both parameters, the full return shape including attribution and enriched_fields, and compliance obligations. An agent has everything needed to invoke it and interpret the response.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate, and it largely does: it gives concrete ID format examples ('osm_node_12345', 'osm_way_67890', 'demo_001') and explains that auto_enrich_website triggers a search only when the website is absent from OSM tags. It omits the default value of true, which the schema does carry.

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?

States a specific verb and resource ("Retrieve enriched details for a specific business by ID") and the lookup-by-ID framing distinguishes it from the search_businesses sibling. It stops short of naming siblings explicitly, so the differentiation is implied rather than stated.

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?

There is no explicit when-to-use or when-not-to-use guidance, and no routing to alternatives such as search_businesses (to find an ID first) or find_business_website (the dedicated website lookup). The agent must infer the workflow position from the parameter name alone.

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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