get_lender_directoryWho finances whom: directory of 8,600+ UCC secured parties (lenders) ranked by filing volume, with active-lien counts and lapsing-soon exposure per lender. California + Colorado coverage (CO refreshed daily). No API key required.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Lender name search (e.g. 'Wells Fargo') | |
| page | No | Page (50/page). Default: 1 |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds valuable behavioral context beyond that: no API key required, California + Colorado coverage, and daily refresh for Colorado. This is useful operational detail 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded with the core value proposition, then coverage, freshness, and auth requirements. Every sentence adds relevant information, and there is no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only directory tool with two optional parameters and no output schema, the description provides enough context: what the directory contains, ranking criteria, metrics included, geographic coverage, refresh cadence, and authentication requirements. It does not specify return format, but the described contents are sufficient for invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 and page with examples and defaults. The description adds no additional parameter-level semantics, but the high schema coverage makes that acceptable. The description's content details are about the returned data, not the parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific resource ('directory of 8,600+ UCC secured parties (lenders)') and clear capabilities: ranking by filing volume, active-lien counts, and lapsing-soon exposure. 'Who finances whom' and 'lenders' clearly differentiate it from sibling tools like get_broker_directory.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives context on coverage and data freshness but does not explicitly say when to use this tool versus alternatives like get_broker_directory or get_money_in_motion. There are no exclusion conditions or direct sibling comparisons, so usage guidance is implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.