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datasets_trustmrr_search

Search the TrustMRR dataset. Searches public startups with payment-provider-verified revenue and MRR, stored in a search index.

Instructions

Search the TrustMRR dataset. Searches public startups with payment-provider-verified revenue and MRR, stored in a search index. Filter by category, country, payment provider, target audience, tech, marketing channel, listing tier and for-sale status, and by revenue/MRR/traffic/growth/multiple/asking-price ranges. Sort enum: relevance, mrr_desc, revenue_desc, revenue_30d_desc, traffic_desc, growth_desc, deal_score_desc, price_asc, price_desc, multiple_asc, founded_desc. status enum: active, removed. Billing: this search costs 1 credit per record returned, with a minimum of 2 (an empty result costs 2, a full page of 100 costs 100).

Input Schema

NameRequiredDescriptionDefault
qNoFull-text query over name, description, seller message and business summary, max 256 characters
pageNoPage number, defaults to 1
slugNoExact startup slug filter, max 128 characters
sortNoSort enum: relevance, mrr_desc, revenue_desc, revenue_30d_desc, traffic_desc, growth_desc, deal_score_desc, price_asc, price_desc, multiple_asc, founded_desc
techNoDetected tech-stack slug filter (e.g. nextjs, reactnative), max 128 characters
statusNoLifecycle enum: active, removed
channelNoDetected marketing-channel slug filter (e.g. meta-ads, seo), max 128 characters
countryNoExact ISO country-code filter (e.g. US), max 128 characters
max_mrrNoMaximum verified MRR in USD
min_mrrNoMinimum verified MRR in USD
on_saleNoFilter for startups currently listed for sale
categoryNoExact category filter (e.g. SaaS, Artificial Intelligence, Mobile Apps), max 128 characters
page_sizeNoPage size, defaults to 20 and maxes at 100; page * page_size must be <= 10000
min_growthNoMinimum 30-day revenue growth percentage
min_revenueNoMinimum verified all-time revenue in USD
min_trafficNoMinimum last-30-days traffic (visits)
is_sponsoredNoFilter for sponsored (paid-placement) listings
listing_tierNoFor-sale listing-tier filter (e.g. pro), max 128 characters
max_multipleNoMaximum asking-price-to-revenue multiple
business_typeNoBusiness-type filter (e.g. B2B, B2C), max 128 characters
min_ahrefs_drNoMinimum Ahrefs Domain Rating
min_revenue_30dNoMinimum verified last-30-days revenue in USD
target_audienceNoTarget-audience filter (e.g. B2B, B2C), max 128 characters
max_asking_priceNoMaximum asking price in USD
min_asking_priceNoMinimum asking price in USD
payment_providerNoPayment-provider filter (e.g. stripe, revenuecat, superwall, creem), max 128 characters

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.17.5
    • addedInput schema / properties / sort / enum
      Added value: +[
+  "relevance",
+  "mrr_desc",
+  "revenue_desc",
+  "revenue_30d_desc",
+  "traffic_desc",
+  "growth_desc",
+  "deal_score_desc",
+  "price_asc",
+  "price_desc",
+  "multiple_asc",
+  "founded_desc"
+]
    • addedInput schema / properties / status / enum
      Added value: +[
+  "active",
+  "removed"
+]
  2. Addedv1.5.0

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the burden and does well: it discloses the billing model (1 credit per record, minimum 2, empty result costs 2, full page of 100 costs 100) and that data is stored in a search index. It does not mention pagination limits beyond the schema, but the cost model is a major behavioral disclosure that would otherwise be invisible.

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

Conciseness4/5

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

Front-loads the core purpose and then org anizes filters and sort into enumerable lists. Slightly dense with the long filter enumeration, but every sentence carries information; the billing sentence is essential.

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 26-parameter search tool with no output schema and no annotations, the description covers the data domain, filterable fields, sort options, and the critical cost model. It could state when to prefer this over sibling search tools, but it is otherwise complete enough to call correctly.

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 description coverage is 100%, so parameters are already fully documented in the schema. The description enumerates the filterable dimensions and sort enum, which adds navigation value but no syntax or format details beyond the 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?

States a specific verb (Search) and resource (TrustMRR dataset / public startups with verified revenue and MRR), and the description adds qualifying detail (payment-provider-verified revenue) that distinguishes it from sibling datasets searches.

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

Usage Guidelines3/5

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

The description lists many filter dimensions and sort enums, implying usage, but never states when to use this tool versus alternatives like datasets_trustmrr_item, trustmrr_search, datasets_trustmrr_facets, or the many other datasets_*_search siblings. No when-not guidance is given.

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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