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Stratalize Healthcare

get_value_based_care_performance

Read-only

Use when benchmarking VBC contract performance, assessing FFS-to-VBC transition readiness, or preparing a population health strategy presentation. Returns MSSP ACO savings rates, BPCI episode costs, and MIPS quality signal medians. Example: MSSP Track 1 ACOs generating median 2.3% savings above benchmark — top quartile at 4.8% — organizations below 1.5% savings face program exit risk. Source: CMS VBC program data composite.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bed_sizeNo
specialtyNo
program_typeNo
current_vbc_revenue_pctNoPercentage of revenue from value-based contracts

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable context beyond this: the specific metrics returned, an illustrative example with concrete values, and the data source (CMS VBC program data composite). It does not contradict annotations.

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 three sentences long, front-loaded with the primary use case, followed by the output specifics and a concrete example. Every sentence adds value, and the example is illustrative without being redundant.

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

Completeness3/5

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

With no output schema, the description gives some sense of return values (savings rates, episode costs, quality medians) and an example, but it lacks details on how the optional parameters filter the results or affect the output. For a tool with four parameters and no output schema, this is a notable gap.

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

Parameters2/5

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

Schema description coverage is only 25% (only one parameter documented). The description does not explain how parameters like bed_size, specialty, or program_type affect the output. It only implies program_type via the mentioned programs (MSSP, BPCI, MIPS), but does not clarify how to use the parameters to shape the query.

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 clearly states the tool returns specific VBC performance metrics (MSSP ACO savings rates, BPCI episode costs, MIPS quality signal medians) with a specific verb ('Returns') and resource ('VBC contract performance'). It distinguishes from sibling tools by focusing on value-based care performance.

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?

Provides explicit use cases: benchmarking VBC contract performance, assessing FFS-to-VBC transition readiness, and preparing population health strategy presentations. It does not mention alternatives or exclusions, but the stated scenarios are clear and distinct from sibling tools.

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

A3.8/5.0
Disambiguation2/5

Several tools have overlapping or nearly identical purposes, such as get_drug_adverse_events and get_openfda_adverse_events both pulling FAERS data, get_drug_recall_status and get_fda_recall_history both handling recalls, and get_cms_star_rating overlapping with get_hospital_care_compare_quality. The distinctions rely on subtle source differences or output formatting, making it easy for an agent to select the wrong tool.

Naming Consistency5/5

All 29 tools follow a strict get_<domain>_<descriptor> pattern, with snake_case throughout. The naming is highly predictable and consistent, which helps agents infer functionality even if they haven't seen a specific tool before.

Tool Count3/5

29 tools is on the heavy side for a healthcare data server, but the breadth of healthcare domains (pharma, providers, payers, supply chain, quality) partially justifies the count. However, the presence of overlapping tools suggests the count could be reduced by consolidation without losing coverage.

Completeness4/5

The tool surface covers a wide range of healthcare operations: financial benchmarks, drug safety, compliance, quality ratings, provider verification, supply chain, and value-based care. Minor gaps exist (e.g., no specific patient outcome benchmark tool), but overall the core workflows for healthcare intelligence and benchmarking are well represented.

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