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Insurance Claim Parser

Parse Insurance Claim

parse_claim

Parse unstructured insurance claim text (email, form, notes) into structured JSON: claim number, type, policy holder, incident, amounts, status, adjuster, next actions, risk flags. Useful for insurers, brokers, claims processing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
claim_textYesRaw claim text (email, form, notes)
claim_typeNoClaim type hint: auto, home, life, health, property, liability, workers_comp

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It does disclose the output shape (claim number, type, policy holder, incident, amounts, status, adjuster, next actions, risk flags), which is real value given there is no output schema, but it says nothing about permissions/auth, rate limits, failure modes, or how malformed or non-claim text is handled.

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?

Two sentences, front-loaded with the action and the output field inventory; nothing is buried. The trailing audience clause ('useful for insurers, brokers, claims processing') is filler that does not earn its place, keeping this short of a 5.

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 low-complexity, two-parameter (one required), flat tool with no output schema and no annotations, the description covers the essential contract: input sources and the resulting structured fields. It stops short of covering edge-case behavior such as unparseable input, which leaves a modest gap.

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 both parameters are already documented; the baseline is 3. The description reinforces the sources of claim_text (email, form, notes) but never mentions the claim_type hint parameter or its allowed values, so it adds no meaning beyond the schema.

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 verb (parse) plus the exact resource (unstructured insurance claim text from email/form/notes) and the target format (structured JSON) with an enumerated field list. An agent immediately knows what goes in and what comes out; there are no siblings to disambiguate against.

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 only usage signal is an audience list ('useful for insurers, brokers, claims processing'), which is marketing rather than guidance. There is no when-to-use, when-not-to-use, prerequisite, or alternative-tool advice, so the agent must infer the trigger condition itself.

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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