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Taco Street Locating

Request a free human locator

request_locator_help

Hand the renter to Alexander, Taco Street's human locator, who confirms live pricing/availability and books tours — free to renters (properties pay locators). Requires the renter's real phone or email, shared with their consent. One request per renter.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityNo
nameNo
noteNoAnything the locator should know.
agentNoName of the referring assistant/product.
contactYesThe renter's phone number or email (with their consent).
move_inNoMove-in timeline, e.g. 'November'.
bedroomsNo
budget_maxNo
neighborhoodsNo

Schema Changelog

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

  1. First observed

TDQS

B3.2/5.0
Behavior4/5

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

With no annotations, the description carries the full transparency burden. It discloses consent requirements, the cost model (properties pay), and the one-request-per-renter limit, which are important behavioral traits beyond a simple request action.

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 concise sentences with no filler; each sentence adds a distinct piece of information: the locator's role, the cost/consent model, and the per-renter limit. The key action is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 9-parameter tool with no annotations and no output schema, the description is not complete enough. It provides strong high-level context but lacks parameter-level guidance and clear routing criteria, so an agent cannot fully determine how to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 44%, so the description needs to compensate. It adds meaning for contact by explaining it must be a real phone/email shared with consent, but it does not clarify city, name, move_in, budget, or neighborhoods, leaving most parameters underspecified.

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 clearly states the action: hand the renter to Alexander, who confirms live pricing/availability and books tours. It names a specific resource and verb, but it does not explicitly distinguish itself from the similar sibling tool ask_a_locator.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives constraints such as 'free to renters' and 'one request per renter,' but it does not say when to use this tool versus ask_a_locator or other siblings. No alternatives or exclusions are mentioned, so usage timing is left to inference.

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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