Skip to main content
Glama
pgang002

nfip-mcp-server

by pgang002

search_claims

Search NFIP flood claims by state, flood event, year, or minimum building payment. Retrieve matching claims and counts from FEMA data.

Instructions

Search NFIP flood claims with optional filters. All filters are combined with AND; omit any filter you don't need.

Args: state: Two-letter US state code, e.g. 'FL', 'NJ'. flood_event: Substring match against the named flood event, e.g. 'Katrina' matches 'Hurricane Katrina'. min_building_payment: Only return claims where the net building payment was at least this amount. year: Year the loss occurred. limit: Max number of results to return (default 10).

Returns: A dict with the matching claims and how many were found.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNo
limitNo
stateNo
flood_eventNo
min_building_paymentNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden and does it well: it explains AND-combination, substring matching for flood_event, threshold filtering for min_building_payment, the default limit, and that a dict with matching claims and a count is returned. Minor gaps remain around pagination, sorting, and exact response keys.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear opening sentence, an Args block, and a Returns line. It is concise and informative, though the default-limit value is repeated from the schema and the wording 'optional filters' appears twice in slightly different forms.

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 search tool with five optional parameters and no output schema, the description provides enough context to call it correctly: all parameters are documented, filter behavior is clear, and the return type is summarized. The exact structure of the returned dict is not specified, but this is a minor gap.

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

Parameters5/5

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

Schema description coverage is 0%, and the description fully compensates by explaining every parameter: state format, substring matching for flood_event, min_building_payment threshold, year meaning, and limit default. It also adds relational semantics by stating filters are combined with AND.

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 tool searches NFIP flood claims with optional filters, using a specific verb and resource. It does not explicitly differentiate from sibling tools like get_claim or claims_summary, but the meaning is unambiguous.

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 gives useful filter-combination guidance (AND logic, omit unneeded filters) and makes clear the tool is for searching claims. However, it never states when to prefer search_claims over siblings such as get_claim or claims_summary, leaving tool selection mostly implied.

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