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Glama

Fundz

Broker Directory

get_broker_directory
Read-onlyIdempotent

Who broker-of-records whom: directory of 65,000+ benefits brokers from DOL Form 5500 Schedule A Part 1, ranked by filings carried, with sponsor counts and commission volume per broker. For carriers, broker M&A, benefits-tech sellers, and brokers scouting competitors. No API key required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoBroker name search (e.g. 'Lockton')
pageNoPage (50/page). Default: 1
stateNo2-letter US state filter

Schema Changelog

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

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive, so the description does not need to restate safety. It adds useful behavioral context: the data source, the 65,000+ record scope, ranking by filings, and 'No API key required.' It does not cover response format or rate limits, but the annotation coverage lowers the burden.

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 compact: three sentences that each add value, covering dataset, source, ranking, metrics, audience, and auth. The 'Who broker-of-records whom' opener is memorable but slightly less direct than leading with 'Directory of benefits brokers.'

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 simple tool with three optional parameters and no output schema, the description communicates the dataset contents, source, ranking approach, key derived metrics, intended use, and authentication requirement. It does not enumerate exact response fields, but the schema covers pagination and the description names the core data attributes.

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 documents q, page, and state. The tool description adds no parameter-specific meaning beyond what the schema provides, so the baseline of 3 applies.

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?

The description clearly identifies the resource: a directory of 65,000+ benefits brokers from DOL Form 5500, with ranking, sponsor counts, and commission volume. It is distinct enough from siblings like get_lender_directory, but it does not explicitly contrast itself with any sibling and lacks a direct action verb like 'search' or 'look up.'

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description names target audiences and scenarios: carriers, broker M&A, benefits-tech sellers, and brokers scouting competitors. This gives implied usage context, but there is no explicit guidance on when to choose this tool over sibling tools or when not to use it.

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.