Taco Street Locating

Search apartments

search_apartments

Read-onlyIdempotent

Search Taco Street's curated apartment inventory in Austin, Dallas or Houston, TX. Returns scored matches with starting prices and a short 'why' per building. Use for long-term rentals only.

Input Schema

TableJSON Schema

Name	Required	Description
`city`	Yes	Metro to search.
`limit`	No	How many scored matches to return (1-10).
`query`	No	What the renter wants, in plain language (e.g. 'walkable, dog-friendly, near downtown').
`bedrooms`	No	Bedroom count the renter needs.
`budget_max`	No	Max monthly rent in USD.
`neighborhoods`	No	Preferred neighborhoods, if the renter named any.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`city`	Yes
`note`	No
`count`	Yes
`apartments`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedSep 8, 2026

addedInput schema / properties / bedrooms / description
```
Added value: +"Bedroom count the renter needs."
```

addedInput schema / properties / limit / description

Added value: +"How many scored matches to return (1-10)."

addedInput schema / properties / neighborhoods / description

Added value: +"Preferred neighborhoods, if the renter named any."

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "properties": {
+    "apartments": {
+      "items": {
+        "additionalProperties": true,
+        "properties": {
+          "city": {
+            "type": "string"
+          },
+          "estimated_price": {
+            "type": [
+              "string",
+              "number"
+            ]
+          },
+          "is_pet_friendly": {
+            "type": "boolean"
+          },
+          "matchScore": {
+            "type": "number"
+          },
+          "name": {
+            "type": "string"
+          },
+          "neighborhood": {
+            "type": "string"
+          },
+          "pricing_as_of": {
+            "type": "string"
+          },
+          "specials_last_known": {
+            "type": "string"
+          },
+          "starting_prices": {
+            "additionalProperties": {
+              "type": "number"
+            },
+            "type": "object"
+          },
+          "website": {
+            "type": "string"
+          },
+          "whyChosen": {
+            "type": "string"
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "city": {
+      "type": "string"
+    },
+    "count": {
+      "type": "integer"
+    },
+    "note": {
+      "type": "string"
+    }
+  },
+  "required": [
+    "city",
+    "count",
+    "apartments"
+  ],
+  "type": "object"
+}

First observedSep 7, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare the tool read-only, idempotent, and non-destructive. The description adds useful behavioral context beyond annotations by explaining that it returns scored matches with starting prices and a per-building rationale, which is not derivable from annotations alone.

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?

Three short sentences, each earning its place: the first defines the action and scope, the second describes the return value, and the third sets the usage boundary. No fluff or repetition of schema content.

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?

Given the rich schema, presence of an output schema, and strong annotations, this description is nearly complete. It conveys the tool's purpose, output highlights, and usage boundary; the only small gap is not explicitly differentiating it from locator-style sibling tools.

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 schema already describes 100% of parameters, so the baseline is 3. The description adds no parameter-specific detail beyond a high-level sense of what the search covers, but it does not need to, because the schema is complete and descriptive.

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 action ('Search'), a precise resource ('Taco Street's curated apartment inventory'), geographic scope (Austin, Dallas, Houston), and what the output contains (scored matches with prices and 'why' notes). This clearly differentiates it from detail, shortlist, and locator sibling tools.

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?

Provides clear context: it is for searching curated long-term rental inventory in specific Texas metros. The phrase 'Use for long-term rentals only' is an explicit boundary, though it does not name sibling alternatives or state when to choose, say, ask_a_locator instead.

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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