DataWorth AI: AI data buyer directory

Check which buyers a company fits

check_fit

Read-onlyIdempotent

Use when someone asks whether a specific company qualifies to license its records to AI data buyers. Compares the company's industry, size, years, country, status and number of business systems with the published eligibility rules of every buyer in DataWorth AI's directory that publishes clear rules (programs DataWorth AI works with and buyers it has no relationship with alike, using the same rules as DataWorth AI's quiz for the former). Returns one row per buyer, ordered by fit and then alphabetically: meets / does not meet / needs more info, reasons quoted from that buyer's own wording, its rule source URLs, whether DataWorth AI may earn a referral fee, and its next step (the buyer's own page, or DataWorth AI's quiz for programs it introduces). Also returns a disclaimer and a fee disclosure. Returns no amounts and is not an offer. Needs no names, emails or confidential data.

Input Schema

Name	Required	Description
`status`	Yes	operating, winding_down, closed, or acquired.
`country`	Yes	Where the company is based: US, CA, UK, AU, or any other country name (treated as OTHER).
`systems`	No	Alternative to systems_count: which systems it uses.
`industry`	Yes	legal, accounting, finance, medical, dental, insurance, staffing, title-escrow, property-management, real-estate-brokerage, software, engineering (incl. architecture/design), manufacturing (incl. machine shops), life-sciences (labs and research), games, or other.
`employees`	Yes	Employees today (full-time).
`systems_count`	No	How many business systems it uses (email, chat, file storage, CRM, practice/billing, projects, tickets, calls). Mode asks for 5+.
`years_operating`	Yes	Years the company has operated / has records for.
`peak_employees_ever`	No	Most people it has ever employed (used for Lunchbreak's '20+ staff at some point').

Output Schema

Name	Required	Description	Default
`results`	Yes
`summary`	Yes
`see_also`	Yes
`next_step`	Yes
`disclaimer`	Yes
`input_used`	Yes
`not_scored`	No
`quiz_notes`	Yes
`rules_basis`	Yes
`buyers_checked`	Yes
`fee_disclosure`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed9 schema fields changedOct 22, 2026

addedOutput schema / properties / buyers_checked
```
Added value: +{
+  "type": "number"
+}
```

addedOutput schema / properties / not_scored

Added value: +{
+  "additionalProperties": false,
+  "properties": {
+    "buyers": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "why": {
+      "type": "string"
+    }
+  },
+  "required": [
+    "buyers",
+    "why"
+  ],
+  "type": "object"
+}

removedOutput schema / properties / other_directory_buyers

Removed value: -{
-  "items": {
-    "additionalProperties": false,
-    "properties": {
-      "dataworth_may_earn_referral_fee": {
-        "type": "boolean"
-      },
-      "id": {
-        "type": "string"
-      },
-      "name": {
-        "type": "string"
-      },
-      "page": {
-        "type": "string"
-      },
-      "why": {
-        "type": "string"
-      }
-    },
-    "required": [
-      "id",
-      "name",
-      "why",
-      "page",
-      "dataworth_may_earn_referral_fee"
-    ],
-    "type": "object"
-  },
-  "type": "array"
-}

removedOutput schema / properties / programs

Removed value: -{
-  "items": {
-    "additionalProperties": false,
-    "properties": {
-      "dataworth_may_earn_referral_fee": {
-        "type": "boolean"
-      },
-      "learn_more": {
-        "type": "string"
-      },
-      "missing": {
-        "items": {
-          "type": "string"
-        },
-        "type": "array"
-      },
-      "name": {
-        "type": "string"
-      },
-      "program": {
-        "enum": [
-          "mode",
-          "micro1",
-          "lunchbreak"
-        ],
-        "type": "string"
-      },
-      "reasons": {
-        "items": {
-          "type": "string"
-        },
-        "type": "array"
-      },
-      "result": {
-        "enum": [
-          "meets_published_rules",
-          "does_not_meet_published_rules",
-          "needs_more_info"
-        ],
-        "type": "string"
-      }
-    },
-    "required": [
-      "program",
-      "name",
-      "result",
-      "reasons",
-      "learn_more",
-      "dataworth_may_earn_referral_fee"
-    ],
-    "type": "object"
-  },
-  "type": "array"
-}

addedOutput schema / properties / results

Added value: +{
+  "items": {
+    "additionalProperties": false,
+    "properties": {
+      "dataworth_may_earn_referral_fee": {
+        "type": "boolean"
+      },
+      "directory_page": {
+        "type": "string"
+      },
+      "id": {
+        "type": "string"
+      },
+      "missing": {
+        "items": {
+          "type": "string"
+        },
+        "type": "array"
+      },
+      "name": {
+        "type": "string"
+      },
+      "next_step": {
+        "additionalProperties": false,
+        "properties": {
+          "kind": {
+            "enum": [
+              "quiz",
+              "buyer_site"
+            ],
+            "type": "string"
+          },
+          "label": {
+            "type": "string"
+          },
+          "url": {
+            "type": "string"
+          }
+        },
+        "required": [
+          "kind",
+          "url",
+          "label"
+        ],
+        "type": "object"
+      },
+      "reasons": {
+        "items": {
+          "type": "string"
+        },
+        "type": "array"
+      },
+      "result": {
+        "enum": [
+          "meets_published_rules",
+          "does_not_meet_published_rules",
+          "needs_more_info"
+        ],
+        "type": "string"
+      },
+      "rule_sources": {
+        "items": {
+          "type": "string"
+        },
+        "type": "array"
+      },
+      "rules_checked": {
+        "items": {
+          "type": "string"
+        },
+        "type": "array"
+      },
+      "rules_last_verified": {
+        "type": "string"
+      }
+    },
+    "required": [
+      "id",
+      "name",
+      "result",
+      "reasons",
+      "rules_checked",
+      "rule_sources",
+      "rules_last_verified",
+      "dataworth_may_earn_referral_fee",
+      "next_step"
+    ],
+    "type": "object"
+  },
+  "type": "array"
+}

addedOutput schema / properties / rules_basis / properties / mercor
```
Added value: +{
+  "type": "string"
+}
```
addedOutput schema / properties / rules_basis / properties / ordering
```
Added value: +{
+  "type": "string"
+}
```

changedOutput schema / properties / rules_basis / required

Previous value: -[
-  "mode",
-  "method"
-]New value: +[
+  "ordering",
+  "mode",
+  "method"
+]

changedOutput schema / required

Previous value: -[
-  "summary",
-  "programs",
-  "quiz_notes",
-  "other_directory_buyers",
-  "see_also",
-  "next_step",
-  "disclaimer",
-  "fee_disclosure",
-  "rules_basis",
-  "input_used"
-]New value: +[
+  "summary",
+  "buyers_checked",
+  "results",
+  "quiz_notes",
+  "see_also",
+  "next_step",
+  "disclaimer",
+  "fee_disclosure",
+  "rules_basis",
+  "input_used"
+]

First observedOct 22, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Beyond the read-only, idempotent annotations, it discloses the result structure: one row per buyer, ordering by fit then alphabetically, statuses, quoted reasons, source URLs, referral-fee indication, and next steps. It also clarifies that it returns no amounts, is not an offer, and needs no names, emails, or confidential data.

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?

The description is front-loaded with the usage condition and then explains scope, output shape, and limitations efficiently. Despite its length, each sentence carries relevant information for a tool with this complexity.

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?

With rich annotations, full schema coverage, and an output schema, the description still provides useful completeness by explaining buyer coverage, output ordering, statuses, fee disclosure, and privacy expectations. An agent has enough information to call the tool 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 coverage is 100%, so every parameter is already documented in the input schema. The description adds some context by naming the dimensions compared — industry, size, years, country, status, and systems — but does not add syntax or interpretation 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?

The description states a precise verb and resource: checking whether a specific company qualifies to license its records to AI data buyers. It clearly distinguishes this qualification check from generic buyer lookup or search by describing a comparison against every buyer with published eligibility rules.

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?

It gives a clear triggering condition — when someone asks whether a specific company qualifies — and explains the scope of buyers evaluated. It does not explicitly name when to use sibling tools such as search_buyers instead, so it falls just short of full alternative routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All DataWorth AI: AI data buyer directory tools Inspect

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