remodeleriq

remodeleriq_get_risk_stats

Read-onlyIdempotent

Get aggregate red-flag statistics from the RemodelerIQ bid corpus — real anonymized data from bids analyzed by the platform. Returns the most common risk flags and their fire rates, average confidence scores, and bid-total distributions, filtered optionally by US state, project type, or bid size. Requires corpus ≥ 500 submissions; cells below 30 submissions are suppressed. Use when a homeowner asks 'how common is a 40% deposit demand?' or 'what do most kitchen-remodel bids get flagged for?'. This is proprietary empirical data available nowhere else.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "remodeleriq_get_risk_stats"
  }
}

Input Schema

Name	Required	Description
`state_code`	No	Two-letter US state code to filter stats (e.g. 'GA', 'TX'). Omit for national data.
`project_type`	No	Project type to filter (e.g. 'kitchen-remodel', 'bathroom-remodel', 'roofing'). Omit for all project types.
`bid_size_bucket`	No	Bid total range to filter. Omit for all sizes.

Output Schema

Name	Required	Description
`_meta`	No
`cell_n`	No	Bids matching your filters.
`filter`	No
`status`	Yes
`corpus_n`	Yes	Total bids in the RemodelerIQ corpus.
`top_flags`	No
`suppressed`	No	True when cell_n < 30 — data withheld to protect granularity.
`avg_bid_total`	No	Average bid total for bids where a total was provided.
`confidence_score`	No

Schema Changelog

Changes observed during successful MCP inspections.

AddedAug 17, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, but the description adds valuable behavioral context: data is 'real anonymized data', requires corpus ≥ 500 submissions, and suppresses cells below 30 submissions. It also notes the data is proprietary, which helps the agent understand availability and reliability. No contradiction with annotations.

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 four sentences, front-loaded with purpose, then outputs, then constraints, then usage examples. The final sentence ('This is proprietary empirical data available nowhere else.') adds motivational context but is slightly tangential to tool invocation, making the description a bit less concise than ideal.

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

Completeness5/5

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

The description covers the tool's purpose, outputs, filtering options, constraints, and usage scenarios. Given the presence of an output schema and strong annotations, it provides a complete picture for an agent to decide when to use the tool and what to expect. No missing critical context.

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?

The input schema already describes all three optional parameters (state_code, project_type, bid_size_bucket) with 100% coverage. The description mentions these filters but does not add syntax or format details beyond the schema. Since schema coverage is complete, a baseline of 3 is appropriate; the description adds only minimal extra meaning.

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 function: 'Get aggregate red-flag statistics from the RemodelerIQ bid corpus' and specifies the outputs (common risk flags, fire rates, confidence scores, bid-total distributions). It distinguishes from sibling tools like analyze_bid or compare_bids by focusing on aggregate statistics across the corpus, not individual bids.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit usage context is provided: 'Use when a homeowner asks "how common is a 40% deposit demand?" or "what do most kitchen-remodel bids get flagged for?"' This clearly tells the agent when to invoke this tool over alternatives. It also explains constraints like the 500-submission corpus requirement and cell suppression below 30 submissions, aiding in appropriate use.

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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