get_stacked_borrowersCompanies with active secured debt (UCC-1) from 2+ distinct lenders and a filing in the last 24 months — proven appetite for layered financing; second-position and refi/consolidation targets. California + Colorado coverage (CO refreshed daily). No API key required.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Company name search | |
| state | No | 2-letter US state filter |
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 establish read-only, idempotent, and non-destructive behavior. The description adds meaningful context beyond this: data scope (California + Colorado), refresh cadence (CO refreshed daily), and the auth requirement (no API key required), all of which help an agent set expectations.
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: it states the defining criteria first, then the use case, then coverage/access details. Every sentence earns its place with no vague filler.
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 lookup with two optional, fully described parameters and no output schema, the description covers the essential facts: what the companies are, why they matter, where they are located, how fresh the data is, and whether authentication is needed. An agent can select and invoke this tool confidently.
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 coverage is 100%, so the baseline is 3. The description adds value by constraining the state parameter to California and Colorado, which is more specific than the schema's generic '2-letter US state filter' and prevents invalid state usage.
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 defines the resource precisely: companies with active UCC-1 secured debt from 2+ distinct lenders and a filing in the last 24 months. This clear criteria-based definition distinguishes it from sibling tools like get_refinancing_windows and get_money_in_motion without ambiguity.
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 strong usage context: it positions the tool for finding layered-financing appetite and second-position/refi/consolidation targets. It also notes geographic coverage and that no API key is required, but it does not explicitly name sibling alternatives or state 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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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.