signals

funding_events

Read-only

List funding events ordered by announcement date, newest first. Filter by round, amount range, company HQ country, industry group, or a specific company id. Costs one credit per returned record.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Maximum records to return (1-100). Each returned record costs one credit.
`round`	No	Funding round filters. Canonical values: pre-seed, seed, angel, series-a, series-b, series-c, series-d, series-e, series-f, series-g, series-h, series-i, growth, extension, bridge, convertible, safe, debt, grant, pre-ipo, secondary, pipe, other.
`since`	No	Return records announced on or after this date (YYYY-MM-DD) or ISO-8601 timestamp.
`until`	No	Return records announced on or before this date (YYYY-MM-DD, inclusive) or ISO-8601 timestamp.
`cursor`	No	Opaque pagination cursor returned as pagination.nextCursor on the previous page.
`companyId`	No	Return only events for this entity-resolved company id.
`countries`	No	Headquarters country codes (ISO alpha-2, OR). Pass "unknown" to include companies with no country on file.
`naicsCode`	No	NAICS 2022 code for the company. Matches the code and everything beneath it, so "51" also returns 5132 and 5173. Sector ranges work: "31-33" returns all manufacturing.
`verticals`	No	High-level sector filters (OR). Canonical values: ai, fintech, saas, devtools, healthcare, climate, robotics, cybersecurity, logistics, commerce, data, crypto, media, education, marketing, telecom, realestate, hardware, gaming, space.
`maxAmountUsd`	No	Maximum funding amount in USD.
`minAmountUsd`	No	Minimum funding amount in USD.
`industryGroups`	No	Company industry groups (OR). Matched literally against the LinkedIn labels. Pass "unknown" for companies with no industry on file.
`employeeBuckets`	No	Company employee-count buckets (OR). Canonical values: 1-10, 11-50, 51-200, 201-500, 501-1000, 1001-5000, 5001-10000, 10001+.

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already establish read-only/open-world/destructive=false. The description adds the cost model ('one credit per returned record') and ordering behavior ('newest first'), which are not visible in annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with purpose and ordering, followed by filters and cost. No redundant or filler content; every sentence earns its place.

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 read-only list tool with rich per-parameter schema docs, the description covers the core action, ordering, common filters, and cost. It omits some available filters (date range, employee buckets, verticals) and pagination, but the schema carries those details, so nothing critical for selection is missing.

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 covers 100% of parameters with descriptions, so baseline is 3. The description's filter list paraphrases some params (round, amount range, countries, industryGroups, companyId) but adds no format or syntax details beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clear verb 'List', specific resource 'funding events', and ordering 'newest first'. Distinguishes from sibling tools like acquisitions and exec_moves by focusing on funding rounds.

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

Usage Guidelines4/5

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

Description states the exact domain (funding events) and enumerates filter dimensions, giving an agent clear context for when to invoke. It doesn't explicitly name sibling alternatives or when-not conditions, so it stops short of a full decision tree.

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

A4/5.0

Disambiguation5/5

Each tool targets a clearly distinct resource or action: event types (acquisitions, funding_events, exec_moves), entities (companies, investors), and watchlist operations. The watchlist CRUD commands are separated by function, and company_timeline is clearly scoped to a single company's full history.

Naming Consistency4/5

Watchlist tools consistently use verb_noun (create_watchlist, get_watchlist, add_to_watchlist), while data lookup tools use plural nouns describing the returned entity or event (companies, investors, funding_events). The mixed convention is readable and predictable, with only company_timeline deviating from both patterns.

Tool Count5/5

With 11 tools, the server is well-scoped for its domain: company/event data search plus watchlist management. Each tool has a defined role and the count feels neither sparse nor bloated.

Completeness4/5

The toolset covers the core domain well: company and investor search, major event types, company timelines, and full watchlist membership management. A notable minor gap is the absence of a delete_watchlist tool, since create/list/get/add/remove exist for watchlists.

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