bulk_audit
Audit up to 20 URLs concurrently. Results sorted worst-first (best leads first).
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| urls | Yes |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
bizintel
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
A3.7/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses concurrency and sorting, but no annotations are provided. The description does not cover safety (idempotency), auth requirements, or potential side effects. Output schema exists but is not detailed here.
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?
Two sentences, every word contributes value, front-loaded with key info. No redundant or vague phrasing.
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 0% schema coverage, the description is too sparse. It omits error behavior, pagination, and the meaning of 'worst-first' in the context of lead scoring. Output schema existence reduces burden but still incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter 'urls' has no schema description (0% coverage) and the tool description does not explain URL format, validation, or constraints beyond the 20-count limit. This leaves the agent guessing about input requirements.
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?
Clearly states the tool audits up to 20 URLs concurrently, with a specific sort order. The verb 'audit' and resource 'URLs' are explicit, and the bulk nature distinguishes it from siblings like audit_website.
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?
Implies use for multiple URLs, but does not explicitly exclude single URL scenarios or name alternatives. The context is clear but lacks direct when-to-use guidance.
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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TDQS
B3.3/5.0
Disambiguation5/5
Each tool has a clearly distinct purpose: audit vs bulk audit, finding leads with specific conditions, searching, getting details, tech stack, and lead scoring. No overlap that would cause confusion.
Naming Consistency5/5
All tool names follow a consistent verb_noun snake_case pattern (e.g., audit_website, find_no_booking, get_business_details). Verbs are descriptive and nouns clearly indicate the target.
Tool Count5/5
With 8 tools, the set is well-scoped for a business intelligence server. It covers core operations without being bloated or insufficient.
Completeness4/5
The tools cover lead discovery, auditing, and scoring comprehensively. Minor gaps: no detailed audit breakdown beyond score, no export or note-saving functionality, but these are not core to the stated purpose.