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property_value_history

Read-onlyIdempotent

Get property value history and tax assessment trends over multiple years. Shows year-by-year market value, land value, improvement value, and percentage change. Use this for questions like "how has this property's value changed?", "what's the appreciation rate?", "show me the tax assessment history", "has this home gone up in value?", or any property valuation trend question. Requires account number (use property_lookup first to find it). Coverage note: the current Montgomery County, TX connector returns demo/sample records only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countyNoCounty name (default: montgomery)montgomery
account_numberYesCounty appraisal district account number

TDQS

A4.3/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description adds meaningful behavioral detail: exactly what fields are returned, the dependency on property_lookup for account_number, and the critical qualifier that the Montgomery County connector returns demo/sample records only. This goes well beyond the 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 compact and front-loaded, starting with the core behavior, then usage examples, then workflow and caveat. The example-question list is slightly redundant after 'any property valuation trend question,' but it supports natural-language matching and each sentence earns its place.

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?

For a read-only two-parameter tool with no output schema, the description provides purpose, output content, a required predecessor, and an important data-quality warning. There are no critical gaps for correct invocation or interpretation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema already covers 100% of parameters with descriptions. The description adds practical meaning for account_number by explaining the lookup prerequisite. County is not elaborated beyond the schema default, but the schema already handles that, so the description's contribution is sufficient for a baseline-plus rating.

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?

Description clearly states a specific action and resource: 'Get property value history and tax assessment trends over multiple years,' with concrete field details (market value, land value, improvement value, percentage change). This distinguishes it from generic property tools, though it does not explicitly contrast with siblings like realestate_home_values or parcel_sales_history.

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?

It gives explicit question-based triggers ('how has this property's value changed?', 'what's the appreciation rate?') and states a prerequisite workflow ('Requires account number (use property_lookup first to find it)'). It also discloses a demo-data caveat. It does not name alternative tools to avoid, so it falls shy of full routing guidance.

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

B3.2/5.0
Disambiguation2/5

Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.

Naming Consistency3/5

The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.

Tool Count1/5

290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.

Completeness4/5

For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.

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