Get the recorded sale history (price + date, no party names) for one parcel by account id. Useful for valuation, appreciation, and comp analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| state | No | 2-letter state code. Coverage: 'MD' (Maryland statewide) or 'TX' (Harris County / Houston only). Defaults to MD. | |
| account_id | Yes | Parcel account id (from parcel_search). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already signal read-only, idempotent, non-destructive behavior, so the description does not need to restate safety. It adds useful behavioral context by specifying exactly what the return contains ('price + date') and what it omits ('no party names'), which is valuable beyond the annotation hints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no filler. The first sentence front-loads the core behavior and output scope; the second adds practical use cases. Every clause earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a low-complexity, read-only tool with two well-documented parameters and rich annotations, the description is complete. It states what is returned, what is not returned, how the parcel is identified, and when the data is useful. No output schema is present, but the description sufficiently conveys the return content.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents account_id as a parcel account id from parcel_search and documents the state parameter including defaults and coverage. The description reinforces the account_id role but does not materially add beyond the schema, so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb ('Get'), a clear resource ('recorded sale history'), and the input key ('by account id'). It also states what is deliberately excluded ('no party names'), which distinguishes it from owner-focused property tools like property_search_owner. The scope is unambiguous: one parcel, price + date, sale history.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear use cases ('valuation, appreciation, and comp analysis') and indicates the single-parcel context by account id. It does not explicitly name sibling alternatives or state when not to use it, but the context is clear enough to guide selection among many related real-estate tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.