CrashStory MCP

get_review_intelligence

Analyze law firm reviews with AI to extract plain-language praise and complaint summaries, pain points, case types, trust signals, and response quality.

Instructions

Get AI-analyzed review intelligence for a law firm. Returns plain-language praise and complaint summaries, 12 consumer pain point dimensions, case type distribution, trust signals, response quality, and highlight reviews.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "firm_slug": "<firm_slug>"
    },
    "name": "get_review_intelligence"
  }
}

Input Schema

Name	Required	Description	Default
`firm_slug`	Yes	Canonical firm slug from search_attorneys (e.g. "levine-law-llc-denver")

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 10, 2026

TDQS

A3.5/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden. While the 'Get' verb implies a read operation, the description does not explicitly state whether the tool is read-only, requires any permissions, or has side effects. It also does not disclose how the response is structured beyond listing components, which is a gap for a tool with no output schema.

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 two sentences: the first front-loads the purpose, the second concisely lists the return components. Every sentence provides value, and there is no filler or repetition of schema information. It is appropriately sized for the tool's complexity.

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

Completeness3/5

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

There is no output schema, so the description must convey what the agent will receive. It lists the high-level categories (pain points, distribution, trust signals, etc.) but does not explain their structure or semantics (e.g., what the 12 dimensions are, how distribution is represented). For a complex AI-derived output, more detail would improve completeness, but the description is minimally adequate.

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 coverage is 100%: the only parameter (firm_slug) is fully described as a canonical slug from search_attorneys. The description adds no additional meaning about the parameter, so it reaches the baseline of 3. There is no need for the description to compensate for schema gaps.

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 clearly states the tool's purpose: to get AI-analyzed review intelligence for a law firm. It enumerates the specific outputs (praise/complaint summaries, 12 pain point dimensions, case type distribution, etc.), which distinguishes it from sibling tools like get_attorney_reviews or get_review_analytics. The verb 'Get' and the resource 'review intelligence' are specific and 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 implies usage when one needs synthesized review intelligence rather than raw reviews or analytics, but it does not explicitly state when to use this tool versus alternatives like get_review_analytics or compare_review_intelligence. There is no exclusion or direct comparison to siblings, so the guidance is only implied by the output list.

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

Deploy Server