MRR Ledger

Get one business profile

get_business

Read-onlyIdempotent

Use when the user names one AI company or agent (e.g. Cursor, Lovable) and asks about its revenue, pricing model or growth. Returns one business's full sourced record. Example: {"slug":"cursor"} -> Cursor with every figure and its source. Includes every dated revenue figure (label, value, as_of, source_url), what it does, model, tier, lessons, growth series, related businesses. Unknown slug -> isError. Cost: free (100 calls/day without a key).

Input Schema

Name	Required	Description	Default
`slug`	Yes	Business slug, e.g. cursor, lovable (from search_businesses or leaderboard).

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 20, 2026

addedInput schema / properties / slug / description

Added value: +"Business slug, e.g. cursor, lovable (from search_businesses or leaderboard)."

Changed5 schema fields changedOct 20, 2026

addedOutput schema / properties / result / properties / referral
```
Added value: +{
+  "type": "boolean"
+}
```

addedOutput schema / properties / result / properties / signup_go_url

Added value: +{
+  "description": "Same link via mrrledger.com/go/<slug> (counts the click, then 302)",
+  "type": "string"
+}

addedOutput schema / properties / result / properties / signup_url

Added value: +{
+  "description": "Referral link (only on businesses with one). Always shown with signup_url_disclosure; never affects figures, ranking, sorting or verification.",
+  "type": "string"
+}

addedOutput schema / properties / result / properties / signup_url_disclosure
```
Added value: +{
+  "type": "string"
+}
```
addedOutput schema / properties / result / properties / signup_url_label
```
Added value: +{
+  "type": "string"
+}
```

First observedOct 18, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Beyond the read-only/idempotent annotations, it discloses return contents (dated revenue figures with label/value/as_of/source_url, growth series, related businesses), error behavior on unknown slug, and cost/rate limit (free, 100 calls/day without a key). That is exactly the operational context annotations cannot carry.

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-loaded with the trigger condition and zero filler, but the enumerated return-field list is dense and partly redundant with the output schema, slightly inflating length.

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?

Covers trigger, scope, payload shape, error semantics, and rate limits; with an output schema present, return-value detail is a bonus rather than a gap. An agent has everything needed to call it correctly on the first attempt.

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 the baseline is 3; the description adds value by showing an input->output mapping ({slug: cursor} -> full Cursor record), confirming the slug is a canonical short identifier rather than an ID or name.

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 and resource ('Returns one business's full sourced record') and grounds it with concrete examples (Cursor, Lovable) plus a literal call example. Clearly distinguishable from list/leaderboard/compare siblings.

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?

Explicit trigger condition: 'Use when the user names one AI company or agent ... and asks about its revenue, pricing model or growth.' It also covers the failure path (unknown slug -> isError). It stops short of naming the sibling to use when no specific company is named, which is left to the schema's slug hint.

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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