OneLence

Business context

onelence_get_business_context

Read-onlyIdempotent

What the team told OneLence about the business: offer, sales cycle, order value, margin, acquisition-cost targets and notes. Entered values, not measurements.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "onelence_get_business_context"
  }
}

Input Schema

Name	Required	Description	Default
`site`	No	Site domain (e.g. example.com) or site id. Defaults to the workspace's newest active site.

Output Schema

Name	Required	Description	Default
`note`	Yes
`site`	Yes
`notes`	Yes
`scope`	Yes
`object`	Yes
`currency`	Yes
`offer_type`	Yes
`updated_at`	Yes
`growth_mode`	Yes
`price_point`	Yes
`sales_cycle`	Yes
`acceptable_cac`	Yes
`learning_budget`	Yes
`gross_margin_pct`	Yes
`market_familiarity`	Yes
`average_order_value`	Yes
`payback_window_days`	Yes
`conversion_readiness`	Yes
`annual_contract_value`	Yes
`lead_to_customer_rate_pct`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedNov 9, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "acceptable_cac": {
+      "anyOf": [
+        {
+          "type": "number"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "annual_contract_value": {
+      "anyOf": [
+        {
+          "type": "number"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "average_order_value": {
+      "anyOf": [
+        {
+          "type": "number"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "conversion_readiness": {
+      "anyOf": [
+        {
+          "type": "string"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "currency": {
+      "anyOf": [
+        {
+          "type": "string"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "gross_margin_pct": {
+      "anyOf": [
+        {
+          "type": "number"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "growth_mode": {
+      "anyOf": [
+        {
+          "type": "string"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "lead_to_customer_rate_pct": {
+      "anyOf": [
+        {
+          "type": "number"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "learning_budget": {
+      "anyOf": [
+        {
+          "type": "number"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "market_familiarity": {
+      "anyOf": [
+        {
+          "type": "string"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "note": {
+      "type": "string"
+    },
+    "notes": {
+      "anyOf": [
+        {
+          "type": "string"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "object": {
+      "const": "business_context",
+      "type": "string"
+    },
+    "offer_type": {
+      "anyOf": [
+        {
+          "type": "string"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "payback_window_days": {
+      "anyOf": [
+        {
+          "type": "number"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "price_point": {
+      "anyOf": [
+        {
+          "type": "string"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "sales_cycle": {
+      "anyOf": [
+        {
+          "type": "string"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "scope": {
+      "enum": [
+        "site",
+        "workspace",
+        "none"
+      ],
+      "type": "string"
+    },
+    "site": {
+      "anyOf": [
+        {
+          "properties": {
+            "domain": {
+              "type": "string"
+            },
+            "id": {
+              "type": "string"
+            }
+          },
+          "required": [
+            "id",
+            "domain"
+          ],
+          "type": "object"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "updated_at": {
+      "anyOf": [
+        {
+          "type": "string"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    }
+  },
+  "required": [
+    "object",
+    "scope",
+    "site",
+    "note",
+    "offer_type",
+    "market_familiarity",
+    "conversion_readiness",
+    "growth_mode",
+    "sales_cycle",
+    "price_point",
+    "currency",
+    "average_order_value",
+    "annual_contract_value",
+    "gross_margin_pct",
+    "acceptable_cac",
+    "lead_to_customer_rate_pct",
+    "payback_window_days",
+    "learning_budget",
+    "notes",
+    "updated_at"
+  ],
+  "type": "object"
+}

First observedOct 4, 2026

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world behavior, so the safety profile is covered. The description adds genuinely useful context beyond that: these are self-reported figures entered by the team rather than observed data, which tells the agent how much to trust them. It does not say what is returned when nothing has been entered yet.

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 short sentences with zero filler; the content list is front-loaded and the 'entered values, not measurements' caveat is placed as a compact qualifier. Nothing could be removed without losing information.

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 simple single-parameter read with full annotations, complete schema coverage, and an output schema, the description covers what the tool returns and the nature of the data. The only meaningful omission is the empty/unconfigured case and an explicit pointer to the update sibling for changing these values.

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?

There is a single optional 'site' parameter with 100% schema coverage, including the default-to-newest-active-site behavior. The description adds no further meaning about site scoping, so the schema does all the work and the baseline of 3 applies.

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

Purpose4/5

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

The description names the specific resource and enumerates its contents (offer, sales cycle, order value, margin, acquisition-cost targets, notes), so an agent knows exactly what comes back. It lacks an explicit verb like 'retrieve' and does not name onelence_update_business_context as the write counterpart, but the get/update naming plus the field list makes the purpose unambiguous.

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?

Usage is only implied: an agent infers it should call this before reasoning about targets, margin, or CAC. The phrase 'Entered values, not measurements' implicitly steers away from the measurement-oriented siblings such as onelence_get_measurement, but no tool is named and no when-not condition (e.g. data not yet entered) is stated.

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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