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@striderlabs/mcp-statefarm

statefarm_find_agent

Locate State Farm insurance agents near any city, state, or ZIP code. Filter by language, insurance specialization, and search radius to get contact info and office hours.

Instructions

Find State Farm insurance agents near a location. Returns a list of local agents with contact info, office hours, languages spoken, and specializations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languageNoPreferred language for the agent (e.g. Spanish, Mandarin)
locationYesCity, state, or ZIP code to search near
max_resultsNoMaximum number of agents to return (default: 5, max: 20)
radius_milesNoSearch radius in miles (default: 10)
insurance_typeNoInsurance type specialization to filter by (default: any)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It communicates that this is a read-style lookup and lists the output fields, but it does not mention ordering, data freshness, pagination, access requirements, or confirm there are no side effects.

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, front-loaded sentence with no filler. It states the action, the target, the location scope, and the key output fields efficiently.

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 location-based agent search, the description plus fully documented schema provides most of what an agent needs to invoke the tool. Since there is no output schema, the description's return-field summary is valuable, but minor gaps such as result ordering and geocoding behavior keep it from being fully complete.

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 description does not need to restate parameter definitions. It lightly reinforces the language and insurance_type parameters by mentioning 'languages spoken' and 'specializations,' but adds no meaningful semantic detail beyond the schema.

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 uses a specific verb and resource: 'Find State Farm insurance agents near a location.' It clearly identifies the tool as an agent-locator service and is distinguishable from siblings like claims, quotes, and payments, though it does not explicitly name an alternative.

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?

There is no explicit guidance on when to use this tool versus alternatives. The phrase 'near a location' implies a location-based lookup, but the description does not state when an agent should prefer this over get_quote, policy_details, or other sibling tools.

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