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Fundz

by io.github.Fund-z

Money in Motion

get_money_in_motion
Read-onlyIdempotent

Companies with an executive-move announcement in the last 180 days AND a verified funding round in the last 24 months — the wealth advisor's 'money in motion' moment: a new comp package (often equity), an old 401(k) to roll over, benefits about to be re-evaluated. Score-ranked, refreshed daily. For RIAs, wealth managers, retirement-plan advisors, executive-benefits sellers. No API key required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoCompany name search
stateNo2-letter US state filter (e.g. 'CA')

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.3/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/idempotent annotations, the description adds concrete behavioral details: data is 'Score-ranked, refreshed daily,' has a 180-day/24-month lookback window, and requires 'No API key.' This gives an agent useful expectations about recency, ranking, and access without contradicting the annotations.

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?

The description is efficiently packed with the core criteria first, followed by the use-case explanation and audience. The 'money in motion' explanation is slightly colorful but earns its place by clarifying why this data matters. No filler or redundancy.

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 description conveys what is returned (companies meeting two conditions), the ranking mechanism, refresh cadence, intended audience, and authentication requirement. There is no output schema, so return-field detail is limited to 'score-ranked,' but that is enough to select and invoke the tool correctly.

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 input schema already documents both optional parameters with 100% coverage: q is 'Company name search' and state is a '2-letter US state filter.' The description adds no additional parameter-level detail, so the baseline of 3 applies for a fully schema-covered parameter set.

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?

The description states a precise resource and selection criteria: companies with an executive-move announcement in the last 180 days AND a verified funding round in the last 24 months. This clearly differentiates the tool from all siblings, which target benefits, brokers, lenders, refinancing, and hiring. The verb 'get' and the noun phrase 'Companies with...' make the operation obvious.

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?

The description names a clear target audience—RIAs, wealth managers, retirement-plan advisors, executive-benefits sellers—and explains the 'money in motion' scenario that triggers use. It does not explicitly contrast with sibling tools like get_funded_and_hiring, but the audience and use case provide strong contextual 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

A4.1/5.0
Disambiguation4/5

Most tools target clearly distinct signals, but get_funded_and_hiring and get_money_in_motion both identify recently funded companies with an additional activity signal, and the UCC-derived tools share underlying lender/borrower data. Descriptions are detailed enough to resolve most ambiguity, though an agent could briefly hesitate between those pairs.

Naming Consistency5/5

All tool names follow the same get_<descriptive_noun_phrase> pattern, which is predictable and readable for a data-query API. The naming convention is consistent across the entire set.

Tool Count5/5

Seven tools is well-scoped for a lead-generation and prospecting data server. Each tool represents a distinct buying signal or directory lookup, and none feel redundant or purely decorative.

Completeness4/5

The tool set covers the key prospecting triggers advertised: benefit plan activity, funded companies with hiring or executive moves, broker/lender directories, refinancing windows, and stacked borrowers. A minor gap is the lack of a more generic company/funding search or contact-level data, but the core workflow of finding qualified prospects is well covered.

Resources