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parcel_search

Read-onlyIdempotent

Search property parcels by street address and get assessed value, land use, and most recent sale for each match. Coverage: Maryland statewide (all 24 jurisdictions, includes sale prices) and Harris County, TX / Houston (appraised value only, no sale prices since Texas is a non-disclosure state). Returns valuation and characteristics only, not owner names. Use parcel_details for the full record.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax rows (default 10, max 50).
queryYesStreet address fragment, e.g. '100 Main St' or 'Charles St'.
stateNo2-letter state code. Coverage: 'MD' (Maryland statewide) or 'TX' (Harris County / Houston only). Defaults to MD.
countyNoOptional county name to narrow results, e.g. 'Baltimore', 'Montgomery'.

TDQS

A4.4/5.0
Behavior4/5

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 useful behavioral context: it returns only valuation and characteristics, never owner names, and sale-price availability varies by state. This goes beyond the structured annotations without contradicting them.

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 compact and front-loaded: purpose, returned fields, coverage caveats, exclusions, and the sibling alternative all appear in three focused sentences. No sentence is wasted or redundant with the schema.

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

Completeness4/5

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

For a read-only list-style tool with annotations and full schema coverage, the description covers what is returned, what is not returned, geographic coverage, and a pointer to the fuller sibling. Without an output schema, slightly more detail about the exact result shape could be ideal, but nothing essential is missing for selecting and invoking the tool.

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?

Schema description coverage is 100%, so the schema already documents all four parameters. The description adds interpretive value by clarifying that 'query' is a street-address fragment, that state coverage differs, and that sale-price availability depends on the state selected, which helps an agent choose parameter values correctly.

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 opens with a specific verb and resource: 'Search property parcels by street address' and lists the exact returned fields (assessed value, land use, most recent sale). It also distinguishes itself from a sibling by saying 'Use parcel_details for the full record,' so an agent can tell it apart from related parcel tools.

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 clear context for when to use this tool by stating coverage limits (Maryland statewide vs. Harris County, TX/Houston), explicitly noting that Texas returns appraised value only and no sale prices, and pointing to parcel_details as the alternative for full records. It does not directly compare against other siblings like parcel_sales_history, so it falls just short of exhaustive 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.

Resources