GeezerKeeper Referral Exit Routes and Deadlines

Get canonical GeezerKeeper business information

get_info

Read-onlyIdempotent

Return canonical public business identity, contact, pricing range, and privacy-boundary information (PUBLICMCP v0.3). Accepts no inputs.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Output Schema

TableJSON Schema

Name	Required	Description	Default
`logo`	Yes
`name`	Yes
`contact`	Yes
`tagline`	Yes
`website`	Yes
`category`	Yes
`description`	Yes
`price_range`	Yes
`business_type`	Yes
`data_boundary`	Yes
`opening_hours`	Yes
`social_profiles`	Yes
`publicmcp_version`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedJul 24, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "https://json-schema.org/draft/2020-12/schema",
+  "additionalProperties": false,
+  "properties": {
+    "business_type": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "category": {
+      "type": "string"
+    },
+    "contact": {
+      "additionalProperties": false,
+      "properties": {
+        "email": {
+          "format": "email",
+          "pattern": "^(?!\\.)(?!.*\\.\\.)([A-Za-z0-9_'+\\-\\.]*)[A-Za-z0-9_+-]@([A-Za-z0-9][A-Za-z0-9\\-]*\\.)+[A-Za-z]{2,}$",
+          "type": "string"
+        },
+        "quote_url": {
+          "format": "uri",
+          "type": "string"
+        },
+        "telephone": {
+          "type": "string"
+        }
+      },
+      "required": [
+        "quote_url",
+        "email",
+        "telephone"
+      ],
+      "type": "object"
+    },
+    "data_boundary": {
+      "additionalProperties": false,
+      "properties": {
+        "accepts_personal_information": {
+          "type": "boolean"
+        },
+        "submits_requests": {
+          "type": "boolean"
+        }
+      },
+      "required": [
+        "accepts_personal_information",
+        "submits_requests"
+      ],
+      "type": "object"
+    },
+    "description": {
+      "type": "string"
+    },
+    "logo": {
+      "format": "uri",
+      "type": "string"
+    },
+    "name": {
+      "type": "string"
+    },
+    "opening_hours": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "price_range": {
+      "type": "string"
+    },
+    "publicmcp_version": {
+      "type": "string"
+    },
+    "social_profiles": {
+      "additionalProperties": {
+        "type": "string"
+      },
+      "propertyNames": {
+        "type": "string"
+      },
+      "type": "object"
+    },
+    "tagline": {
+      "type": "string"
+    },
+    "website": {
+      "format": "uri",
+      "type": "string"
+    }
+  },
+  "required": [
+    "publicmcp_version",
+    "name",
+    "tagline",
+    "description",
+    "website",
+    "business_type",
+    "logo",
+    "price_range",
+    "contact",
+    "opening_hours",
+    "social_profiles",
+    "category",
+    "data_boundary"
+  ],
+  "type": "object"
+}

First observedJul 21, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds context by noting the information is public and canonical, and explicitly mentions 'privacy-boundary information,' indicating what is not included (private data). This goes beyond the structured annotations, enhancing transparency.

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 a single concise sentence, front-loaded with the action and key details. It includes only necessary information (what is returned, public nature, version tag, no inputs) without any fluff or redundancy.

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?

Given the tool's low complexity (no parameters), rich annotations (read-only, idempotent), and an output schema, the description is fully sufficient. It covers what information is returned and notes the privacy boundary, leaving no critical gaps for an agent to invoke this tool correctly.

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?

The tool has zero parameters, and the schema fully captures this (100% coverage). Per the rubric, 0 params earns a baseline of 4. The description repeats 'Accepts no inputs,' which confirms but does not add meaning beyond the schema. This is acceptable and consistent.

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 clearly states the tool returns canonical public business identity, contact, pricing range, and privacy-boundary information. The verb 'Return' and specific resource categories make the purpose unambiguous. It distinguishes from sibling tools like get_location and get_services by focusing on canonical business-level information.

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 implies usage when one needs the listed business information, but it does not explicitly mention alternatives or when not to use it. Sibling tool names provide context, but the description lacks direct guidance on tool selection, so usage is only inferred.

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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