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remodeleriq

by io.github.gustavoatar

remodeleriq_analyze_bid

Read-onlyIdempotent

Analyze a home-remodeling contractor's bid/estimate for fairness and risk. Returns a 0-100 confidence score, score_breakdown by dimension, red flags with stable flag IDs (deposit traps, vague scope, missing items, payment terms), a plain-English summary, and negotiation talk tracks. Score rubric: contract clarity 40%, scope completeness 30%, price realism 30%. Use when a homeowner asks 'is this contractor quote fair?' or shares a remodeling estimate. Note: analyses may contribute de-identified data to aggregate remodeling-risk research.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bid_textYesThe full text of the contractor's bid/estimate (line items, terms, scope).
bid_totalNoThe total dollar amount of the bid, if known.
state_codeNoTwo-letter US state code (e.g. 'TX', 'GA') for localized labor/legal context. Defaults to GA.
finish_tierNoQuality tier of finishes specified in the bid.
scope_depthNoDepth of work: cosmetic (paint/fixtures only), pull_and_replace (replace but not reconfigure), full_gut (down to studs), addition (new square footage).
square_footageNoProject area in square feet, if known (e.g. 200 for a bathroom, 400 for a kitchen).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
_metaNo
summaryYes
verdictYes
red_flagsYes
missing_itemsYes
score_breakdownNo
confidence_scoreYes0–100 bid confidence score
negotiation_talk_trackYes

TDQS

A4.3/5.0
Behavior5/5

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

The description goes well beyond the annotations by detailing the output structure (confidence score, breakdown, red flags with stable IDs), the scoring rubric (40/30/30 weights), and the data usage note about contributing de-identified data to research. This adds meaningful behavioral context that annotations do not cover.

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 dense yet compact, with each sentence serving a purpose: purpose, output details, scoring rubric, usage trigger, and a privacy note. It is well-structured and front-loaded, avoiding unnecessary verbosity.

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?

Given the tool's complexity, the description addresses all key aspects: what it does, what it returns, how the score is calculated, when to use it, and a data usage caveat. The presence of an output schema and full parameter documentation reduces the burden, and the description covers the rest.

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 covers 100% of the parameters with descriptions, so the baseline is 3. The description does not add extra parameter-level detail beyond what the schema provides, but it also does not need to, as the schema is sufficient.

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's purpose with a specific verb ('Analyze') and resource ('bid/estimate'), and defines the scope ('fairness and risk'). It does not explicitly differentiate from the sibling tool 'analyze_bid', which may cause ambiguity, but the name and description still make the function clear.

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

Usage Guidelines4/5

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

The description provides explicit usage context: 'Use when a homeowner asks...' or shares an estimate. It does not mention when not to use it or name alternative tools, but the guidance is clear enough for typical scenarios.

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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TDQS

A3.9/5.0
Disambiguation2/5

The deprecated tools are near-identical duplicates of the new prefixed tools, creating significant overlap. An agent could easily call analyze_bid instead of remodeleriq_analyze_bid, leading to confusion despite the deprecation notes.

Naming Consistency2/5

Two naming patterns coexist: the older unprefixed verbs (analyze_bid, get_cost_estimate) and the newer prefixed verbs (remodeleriq_analyze_bid). This inconsistency is minor on its own but combined with the duplicates it creates a jarring mix.

Tool Count3/5

The raw count of 6 is reasonable, but 3 tools are deprecated duplicates, so the effective unique toolset is only 3. This feels padded and slightly wasteful, though not egregious.

Completeness4/5

The three core functions—bid analysis, cost estimates, and labor rates—cover the primary homeowner needs for remodeling transparency. No obvious lifecycle gaps exist since this is a read/analyze API rather than a CRUD system.

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