get_funded_and_hiringCompanies with a verified funding round in the last 12 months AND live hiring evidence (open ATS roles, executive hires, hiring signals) — the strongest buying window, score-ranked and refreshed daily. No API key required.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Company name search | |
| state | No | 2-letter US state filter (e.g. 'CA') |
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 cover read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable behavioral context: data freshness ('refreshed daily'), result ordering ('score-ranked'), and authentication requirements ('No API key required'). No contradiction with 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 a single, information-dense sentence. It front-loads the core selection criteria, then adds ordering, freshness, and auth details without any filler. Every clause contributes to correct tool usage.
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 list tool with two optional parameters and no output schema, the description explains what results look like conceptually, how they are ranked, how fresh they are, and that no API key is needed. It does not describe the exact return structure, but the absence of an output schema and the simplicity of the tool make this an acceptable minor gap.
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%, with q and state both documented in the input schema. The description does not add material meaning about how parameters behave beyond the schema, so the baseline score of 3 is appropriate.
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 verb-resource relationship: it returns companies matching verified funding in the last 12 months AND live hiring evidence. The criteria are concrete and distinguish this tool from sibling tools like get_benefit_plans_in_play or get_lender_directory, which target different business signals.
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 clear context for when to use it: when targeting funded companies with active hiring signals, described as 'the strongest buying window.' It does not explicitly name alternatives or when-not-to-use conditions, but the specificity of the criteria makes intended usage apparent.
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.