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ureweries

by io.github.pipeworx-io

Get Brewery

get_brewery
Read-onlyIdempotent

Fetch full details for a single brewery by its Open Brewery DB UUID. Returns name, type, street address, city, state, postal code, country, GPS coordinates, phone, and website URL.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesOpen Brewery DB brewery ID (e.g., "b54b16e1-ac3b-4bff-a11f-f7ae4ddc27e1")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesBrewery ID
cityYesCity name
nameYesBrewery name
typeYesBrewery type (e.g., micro, macro, pub)
phoneYesPhone number
stateYesState or province
addressYesFull street address
countryYesCountry name
websiteYesWebsite URL
coordinatesYesGeographic coordinates if available
postal_codeYesPostal/zip code

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "address": {
+      "description": "Full street address",
+      "type": "string"
+    },
+    "city": {
+      "description": "City name",
+      "type": "string"
+    },
+    "coordinates": {
+      "description": "Geographic coordinates if available",
+      "oneOf": [
+        {
+          "properties": {
+            "latitude": {
+              "description": "Latitude coordinate",
+              "type": "number"
+            },
+            "longitude": {
+              "description": "Longitude coordinate",
+              "type": "number"
+            }
+          },
+          "required": [
+            "latitude",
+            "longitude"
+          ],
+          "type": "object"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "country": {
+      "description": "Country name",
+      "type": "string"
+    },
+    "id": {
+      "description": "Brewery ID",
+      "type": "string"
+    },
+    "name": {
+      "description": "Brewery name",
+      "type": "string"
+    },
+    "phone": {
+      "description": "Phone number",
+      "type": "string"
+    },
+    "postal_code": {
+      "description": "Postal/zip code",
+      "type": "string"
+    },
+    "state": {
+      "description": "State or province",
+      "type": "string"
+    },
+    "type": {
+      "description": "Brewery type (e.g., micro, macro, pub)",
+      "type": "string"
+    },
+    "website": {
+      "description": "Website URL",
+      "type": "string"
+    }
+  },
+  "required": [
+    "id",
+    "name",
+    "type",
+    "address",
+    "city",
+    "state",
+    "postal_code",
+    "country",
+    "coordinates",
+    "phone",
+    "website"
+  ],
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "id": "b54b16e1-ac3b-4bff-a11f-f7ae4ddc27e1"
+  }
+]
  3. First observed

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the concrete return fields (name, address, coordinates, etc.), which is useful but does not go beyond what the output schema likely provides. No contradiction occurs.

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, well-structured sentence that front-loads the main action and lists the specific return fields. Every word earns its place; no redundancy or filler.

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?

This is a simple tool with one parameter, detailed schema, comprehensive annotations, and an output schema. The description fully covers what the tool does, what it needs (a UUID), and what it returns, so no critical context is missing.

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?

The input schema already documents the single parameter 'id' with an example and description at 100% coverage. The description reinforces that it's an Open Brewery DB UUID, but adds no new semantic detail 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 uses a specific verb ('Fetch') and resource ('full details for a single brewery'), clearly identifying the tool's scope. It distinguishes itself from siblings like search_breweries or breweries_by_city by focusing on fetching details for a single brewery by UUID.

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?

The description clearly indicates the tool is used when you have a specific brewery UUID, implying a lookup-by-ID context. However, it does not explicitly mention alternatives such as search_breweries or breweries_by_city for discovery, so it lacks exclusions.

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

All ureweries toolsInspect
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TDQS

A3.9/5.0
Disambiguation4/5

Most tools have clear distinct purposes, but ask_pipeworx, ask_pipeworx_grounded, and deep_research overlap in querying Pipeworx data sources. Entity_profile and recent_changes also share some coverage. Overall, agents can differentiate, but a few pairs may cause confusion.

Naming Consistency3/5

Tool names use snake_case for multi-word (e.g., ai_visibility_check) but also single-word verbs (forget, recall, remember). The pattern is not uniform: some are verb_noun, some are just noun or verb. Mixed conventions reduce predictability.

Tool Count3/5

At 33 tools, the set is on the high side for an MCP server. The server covers a broad data-query domain, which justifies many specialized tools, but the count is borderline heavy and includes several meta-tools (discover_tools, suggest_questions). Could be streamlined without losing core functionality.

Completeness4/5

The tool surface is comprehensive for data retrieval across financial, economic, pharmaceutical, real estate, weather, and prediction markets. It includes both single-lookup and comparative tools, plus monitoring via subscriptions. Minor gaps exist (e.g., no direct update/delete for user memory beyond forget), but the core domain is well-covered.

Resources