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StartupHub.ai

by ai.startuphub
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Query funding totals

query_funding
Read-only

Answer how much funding was raised, how many rounds closed, or how many companies were founded, for a country, sector, series, and year. This is the StartupHub /analysis chart as data. Use it for questions like 'how much funding did Canadian startups raise in 2026'. dataset=rounds (default) is announced round capital in that year. dataset=startups is companies founded that year, and the dollar total is what those companies have raised to date, not capital raised during the year. dataset=funds is fund closes. dataset=investors counts firms founded, with no dollar total. country is a full English name: Canada, United States, United Kingdom. group=sector or group=country returns a breakdown. Free, no API key. Returns the count, total USD, a chart URL, and the periods or categories behind the total.

Input Schema

NameRequiredDescriptionDefault
toNoLast year, inclusive. Ignored when year is set. Default the current year. Clamped between from and the current year.
fromNoFirst year, inclusive. Ignored when year is set. Default depends on dataset: rounds 2021, funds 2015, startups 2010, investors 1995. Clamped to the dataset range.
yearNoCalendar year, an integer from 1970 to 2100. Sets both from and to. Example: 2026.
groupNoHow to break the total down. Default time, which is the single undivided total. Depends on dataset: funds and startups have no series; investors allows only time, sector and country. An invalid value becomes time. group=sector counts a company in every sector it carries, so that total is higher.
splitNoOptional second breakdown: one of the dataset dimensions, not equal to group. Anything else means none. The total still excludes double-counting.
bucketNoTime bucket. A single year defaults to year. A range defaults to quarter.
sectorNoSector name, for example Artificial Intelligence or Fintech. Comma-separated for more than one, up to 8.
seriesNoRound stage, dataset=rounds only. Exact, case-sensitive values: Pre-Seed, Seed, Series A, Series B, Series C, Series D, Series E, Series F+, Growth, IPO, Debt, Grant, Strategic, Unlabeled. Comma-separated for more than one. Any other value is dropped.
countryNoFull English country name, or a common alias. Examples: Canada, United States, USA, United Kingdom, UK. Comma-separated for more than one, up to 8.
datasetNorounds = capital announced in the year (default). startups = companies founded that year. funds = funds closed. investors = firms founded.
sectorsNoAlias of sector.
countriesNoAlias of country.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed10 schema fields changed
    • addedInput schema / properties / countries
      Added value: +{
+  "description": "Alias of country.",
+  "type": "string"
+}
    • changedInput schema / properties / country / description
      Previous value: -"Full English country name, or a common alias. Examples: Canada, United States, USA, United Kingdom, UK."New value: +"Full English country name, or a common alias. Examples: Canada, United States, USA, United Kingdom, UK. Comma-separated for more than one, up to 8."
    • changedInput schema / properties / from / description
      Previous value: -"First year, inclusive. Ignored when year is set."New value: +"First year, inclusive. Ignored when year is set. Default depends on dataset: rounds 2021, funds 2015, startups 2010, investors 1995. Clamped to the dataset range."
    • changedInput schema / properties / group / description
      Previous value: -"How to break the total down. Default time, which is the single undivided total. group=sector counts a company in every sector it carries, so that total is higher."New value: +"How to break the total down. Default time, which is the single undivided total. Depends on dataset: funds and startups have no series; investors allows only time, sector and country. An invalid value becomes time. group=sector counts a company in every sector it carries, so that total is higher."
    • changedInput schema / properties / sector / description
      Previous value: -"Sector name, for example Artificial Intelligence or Fintech. Comma-separated for more than one."New value: +"Sector name, for example Artificial Intelligence or Fintech. Comma-separated for more than one, up to 8."
    • addedInput schema / properties / sectors
      Added value: +{
+  "description": "Alias of sector.",
+  "type": "string"
+}
    • changedInput schema / properties / series / description
      Previous value: -"Round stage: Pre-Seed, Seed, Series A, Series B, Series C, Series D, Series E, Series F+, Growth, IPO, Debt, Grant, Strategic. Comma-separated for more than one."New value: +"Round stage, dataset=rounds only. Exact, case-sensitive values: Pre-Seed, Seed, Series A, Series B, Series C, Series D, Series E, Series F+, Growth, IPO, Debt, Grant, Strategic, Unlabeled. Comma-separated for more than one. Any other value is dropped."
    • changedInput schema / properties / split / description
      Previous value: -"Optional second breakdown. The total still excludes double-counting."New value: +"Optional second breakdown: one of the dataset dimensions, not equal to group. Anything else means none. The total still excludes double-counting."
    • changedInput schema / properties / to / description
      Previous value: -"Last year, inclusive. Ignored when year is set."New value: +"Last year, inclusive. Ignored when year is set. Default the current year. Clamped between from and the current year."
    • changedInput schema / properties / year / description
      Previous value: -"Calendar year. Sets both from and to. Example: 2026."New value: +"Calendar year, an integer from 1970 to 2100. Sets both from and to. Example: 2026."
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Goes well beyond the read-only annotations by disclosing no-API-key/free access, the exact return payload (count, total USD, chart URL, periods/categories), and non-obvious edge behavior: that dataset=startups dollars are raised-to-date rather than in-year, that group=sector double-counts and inflates totals, and that split keeps the total de-duplicated.

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?

Long but dense and front-loaded: the core capability and example come first, then per-dataset semantics, then return shape. Nearly every sentence carries decision-relevant information (dataset selection, grouping caveats, return format) rather than filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 12-parameter aggregate tool with no output schema, it explains return values itself and covers the semantic traps (dataset differences, sector double-counting, split de-duplication, invalid-value fallbacks) that an agent must understand to call it correctly. Nothing essential is left to inference.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% so baseline is 3, but the description adds genuine interpretation beyond the schema: what the dollar total means per dataset, the higher total implied by sector grouping, and the annual/series scoping caveats. It does not restate most param mechanics, which the schema already covers.

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?

States a specific capability (aggregate funding/rounds/founded-company counts by country, sector, series, year) with a concrete verb-resource framing and the anchor 'the StartupHub /analysis chart as data'. This clearly distinguishes it from per-entity siblings like get_company_funding and get_startup_funding_history, which return individual records rather than aggregate totals.

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?

Gives an explicit usage context with a worked example question ('how much funding did Canadian startups raise in 2026') and explains which dataset to pick for which question type. It lacks an explicit when-not statement or a direct pointer to the per-company funding siblings, so it stops short of full routing guidance.

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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