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get_base_flood_elevations

Retrieve 100-year flood base elevations near a specified coordinate to determine construction requirements and insurance ratings. Uses coordinates (lat, lon) and searches within about 100 meters.

Instructions

Get Base Flood Elevation (BFE) lines near a location from the NFHL. The BFE is the elevation (in feet above datum) that floodwaters are expected to reach during a 1% annual chance (100-year) flood. Critical for construction requirements and insurance rating. Searches within ~100m of the coordinate. BFEs are not present in all flood zones (e.g. Zone X).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latYesLatitude in decimal degrees (WGS84)
lonYesLongitude in decimal degrees (WGS84)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses the spatial search radius (~100m), the unit/meaning of results (feet above datum, 1% annual chance flood), and an important limitation (BFEs absent in some zones). It does not describe return format or error behavior, but the key operational constraints are clearly surfaced.

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?

Four concise sentences, each contributing essential information: what the tool returns, what BFE means, why it matters, and critical caveats. The description is front-loaded with the main purpose and avoids redundant or filler wording.

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 two-parameter geographic lookup, the description covers the tool's purpose, meaning, relevance, search radius, and a key limitation. There is no output schema, so a bit more detail about the exact structure of the returned features could be helpful, but the description is sufficiently complete for an agent to select and invoke the tool correctly.

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?

Schema description coverage is 100%, so the lat/lon parameters are already documented in detail. The description adds the behavioral context that the search happens within ~100m of the coordinate, but does not add much new semantic meaning beyond what the schema provides. Baseline 3 is appropriate.

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 states a specific verb and resource ("Get Base Flood Elevation (BFE) lines near a location from the NFHL") and explains the meaning of BFE. This clearly differentiates it from sibling tools like get_flood_zone and get_firm_panel, which target different NFHL data. An agent can understand exactly what this tool returns without opening the schema.

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 provides clear context for when to use the tool: it is relevant to construction requirements and insurance rating, searches within ~100m of the coordinate, and notes that BFEs are not present in all flood zones. It does not explicitly list alternative tools or say 'use X instead', but the context is strong enough for an agent to infer appropriate usage relative to the siblings.

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