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wu00112238

CleanSignal MCP

scan_b2b_buying_intent

Scan verified B2B buying signals, active tender RFQs, and GEO AI visibility by industry category to surface real procurement intent and prioritize outreach.

Instructions

Scans real-time verified B2B buying signals, active tender RFQs, and GEO AI visibility ranking (industrial_machinery, logistics, saas, medical_devices, dtc_ecommerce).

Input Schema

NameRequiredDescriptionDefault
categoryYesB2B category (e.g. industrial_machinery, logistics, saas, medical_devices, dtc_ecommerce)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations, the description carries the full burden and does not say whether this is a read-only scan, whether it consumes credits (notable given the sibling buy_cleansignal_credits), or what the output looks like. 'Real-time verified' and 'GEO AI visibility ranking' are unexplained terms that leave the agent guessing about data freshness and shape.

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?

One front-loaded sentence with no filler. The trailing parenthetical duplicates the schema's enum examples, costing a bit of space without adding information.

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?

For a one-parameter read tool with no output schema, the description covers what is scanned and for which categories. It leaves ambiguous whether a single category returns all three signal types or only the matching one, which is the one real gap.

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 single parameter is fully documented in the schema at 100% coverage, so a baseline of 3 applies. The description merely repeats the same category examples verbatim, adding no new semantics such as accepted aliases, case sensitivity, or whether multiple categories can be combined.

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?

Specific verb 'Scans' paired with three named resource types (buying signals, tender RFQs, GEO AI visibility ranking) makes the operation clearly identifiable. It does not differentiate itself from siblings such as filter_social_signals, which covers adjacent signal territory, so an agent must infer the boundary.

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 when-to-use guidance, no exclusions, and no mention of the sibling tools (filter_social_signals, get_commodity_regime, buy_cleansignal_credits) as alternatives. Usage is only weakly implied by the category list.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.