Startups and companies that raised funding recently, verified against public filings and press. Answers questions like 'who raised this week' or 'fintech companies that just raised'. Free.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Lookback window in days | |
| sector | No | Sector filter | all |
| min_amount_usd | No | Minimum raise size in USD |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. It adds useful context by saying results are verified against public filings and press, and that the service is free. However, it does not describe response format, pagination, rate limits, or what 'recently' means operationally beyond the example phrases.
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 short, front-loaded with the main subject, and uses concrete examples efficiently. The 'Free.' sentence is minimal and useful. Every sentence contributes.
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?
Given the tool's simple three-optional-parameter shape, the description is mostly adequate. However, with no output schema and no annotations, the agent is left without knowledge of the return format, result limits, or how this tool differs from raise_lookup and deal_stats. That keeps it from being fully complete.
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?
The input schema covers all three parameters with descriptions, so the baseline is 3. The description's examples add a little extra meaning, like 'this week' implying days=7 and 'fintech' implying sector=fintech, but it does not add new semantics for min_amount_usd or otherwise compensate for schema gaps.
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 clearly states the tool returns startups/companies that recently raised funding and gives concrete example questions. It does not explicitly distinguish itself from the sibling tools deal_stats and raise_lookup, but the resource and recency focus are specific enough for an agent to understand the core purpose.
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 provides example queries that indicate when the tool is appropriate, such as 'who raised this week' or 'fintech companies that just raised'. It does not mention when not to use it or name alternatives, but the usage context is fairly clear.
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.
Each tool targets a distinct query type: aggregate stats, specific company lookup, and recent raises list. There is no meaningful overlap that would cause an agent to select the wrong tool.
All tool names are lowercase noun phrases using underscores (deal_stats, raise_lookup, recent_raises). The naming pattern is uniform and predictable, even though it doesn't follow a verb_noun convention.
Three tools is well-scoped for a focused funding/deal information domain. Each tool serves a distinct purpose and no tool feels redundant or unnecessary.
The set covers the core needs of a deal research assistant: aggregate statistics, specific company verification, and recent activity. A slight gap is a lack of detailed single-deal history or advanced filtering, but the core workflows are complete.