US Brand Intelligence

US Brand Signal CRM Field Mapper

us-brand-signal-crm-field-mapper

Read-only

Turn 1 to 100 buyer-supplied US brand signal rows into unsent HubSpot, Salesforce, or generic CRM field proposals while preserving evidence IDs and URLs. No CRM credentials, network calls, proxy, LLM, identity resolution, or arbitrary expressions. — $0.05/call, x402 (USDC on base).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`rows`	Yes	One to one hundred rows with explicit opaque entity IDs and source evidence.
`target`	Yes	Select the closed target field vocabulary; this Actor does not call the target CRM.	hubspot
`mapping`	Yes	Each source field may appear once and must map to the exact target field allowed for the selected CRM.
`schemaVersion`	Yes	The only supported closed contract version.	1.0

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description reinforces this with 'unsent' and explicit negations ('No CRM credentials, network calls...'). It adds behavioral context about preserving evidence IDs/URLs and the pricing/call model, going beyond the annotations without contradicting them.

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?

A single, information-dense sentence that front-loads the primary function, then lists key exclusions and pricing. Every word earns its place; no fluff or repetition.

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

Completeness4/5

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

The tool is moderately complex with nested rows and mappings, but the schema covers parameter details and the description covers high-level behavior and constraints. With no output schema, a bit more explanation of the 'field proposals' output shape could help, but the current description is sufficient for a mapping tool with strong schema support.

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 explains every parameter in detail. The description does not add new parameter-specific semantics beyond summarizing the batch size and the mapping purpose, which matches the baseline for well-documented schemas.

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?

Description states a specific action: turning 1-100 buyer-supplied US brand signal rows into unsent CRM field proposals for HubSpot, Salesforce, or generic CRM, while preserving evidence IDs and URLs. It clearly distinguishes the tool's scope from siblings by naming the CRM mapping focus and the 'unsent' output nature.

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?

Clearly implies when to use by listing exclusions: no CRM credentials, network calls, proxy, LLM, identity resolution, or arbitrary expressions. This helps an agent understand this is for local, safe transformation only, though it does not explicitly name alternative tools or provide direct 'when to use vs. X' 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

A3.8/5.0

Disambiguation2/5

Many tools have overlapping purposes, e.g., us-brand-signal-metrics-aggregator, us-brand-signal-cross-tabulator, and us-brand-signal-brand-summary all produce counts or summaries of the same type of data. The descriptions are detailed, but the boundaries between analyzers, summarizers, and aggregators are unclear enough that an agent may struggle to pick the right one.

Naming Consistency4/5

Most tools follow a consistent 'us-brand-' prefix with descriptive nouns separated by hyphens (e.g., us-brand-signal-batch-splitter, us-brand-evidence-chronology-builder). The exception is 'pricing_info', which uses an underscore and lacks the prefix, creating a minor inconsistency.

Tool Count3/5

At 21 tools, the set is on the heavier side but still manageable for a complex domain like brand intelligence. Many tools are variations on deterministic signal processing, which could be streamlined, but the count itself is not extreme.

Completeness3/5

The tool set covers a broad pipeline for processing buyer-supplied signals and evidence, including splitting, summarizing, routing, and building payloads. However, it lacks any tools for ingesting or fetching data from external sources (except one federal award snapshot), and there is no end-to-end controller that orchestrates the workflow. This leaves notable gaps for a complete 'brand intelligence' lifecycle.

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