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LiveDataLink

by io.github.blackboxfoundry

ok_wells_search

Read-onlyIdempotent

Search Oklahoma oil & gas wells by county, operator, API number, and/or well type using the Oklahoma Corporation Commission (OCC) RBDMS well database (keyless public record). Unlike a pure map layer, this returns the OPERATOR, well status, well type (OIL/GAS/DRY/etc.), well name, county, and location for each match, plus the total matching count and a by-operator/by-type breakdown. Use it for 'who operates wells in county X', 'how many wells does operator Y have', or an operator's well portfolio. Oklahoma covers the Anadarko Basin / SCOOP / STACK plays. At least one of county, operator, or api is recommended to narrow the search.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
apiNoAPI well number (digits; non-digits are ignored).
limitNoMax well records to return (default 25, max 100). The total matching count is always reported.
countyNoOklahoma county name (e.g. 'Canadian', 'Kingfisher', 'Grady'). Matched case-insensitively.
operatorNoOperator name or fragment (e.g. 'Chesapeake', 'Devon'). Substring, case-insensitive.
well_typeNoFilter by well type: OIL, GAS, DRY, SWD (saltwater disposal), etc.

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds context beyond those flags: the data source (OCC RBDMS), that it is a keyless public record (no auth needed), and that the result includes a total matching count plus by-operator/by-type breakdowns. It doesn't mention pagination or rate limits, but the annotations lower that burden.

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 four sentences with no filler: purpose and data source first, then return-value differentiation, then explicit use cases, then search-narrowing guidance. Every sentence contributes decision-relevant information and the most important content is front-loaded.

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 zero-required-parameter search tool with no output schema, the description covers what an agent needs to select and call it correctly: the data source, filter options, returned fields, aggregate statistics, common use cases, and narrowing advice. No critical information appears missing.

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?

Schema description coverage is 100%, so the schema already documents every parameter. The description adds usage color such as 'at least one filter recommended' and example fragments, but it does not materially extend parameter semantics beyond what the schema already provides. Baseline 3 is appropriate.

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 names the verb and resource precisely: 'Search Oklahoma oil & gas wells' with filter dimensions. It also distinguishes itself from a pure map layer by enumerating the returned fields (operator, well status, well type, well name, county, location) plus a total count and breakdown, making its purpose and scope unmistakable.

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?

Concrete use cases are given: 'who operates wells in county X', 'how many wells does operator Y have', and 'an operator's well portfolio.' It also recommends narrowing with at least one of county, operator, or API. It does not name specific sibling alternatives or state when NOT to use the tool, so it stops short of a 5.

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