Skip to main content
Glama

Stratalize Healthcare

get_ehr_cost_per_bed

Read-only

Use when benchmarking EHR maintenance costs before a contract renewal or evaluating health IT budget efficiency. Returns benchmark cost per licensed bed with optional gap analysis when actual cost and bed count are provided. Example: Epic maintenance median $4,500/bed — 300-bed hospital at $6,200/bed is 38% above market — renegotiation trigger especially strong at 5+ year renewal cycles. Source: KLAS 2024, Kaufman Hall EHR TCO composite.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bed_countNo
annual_costNo
vendor_nameYes

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds behavioral context beyond that: it describes the return type (benchmark per bed), the optional gap analysis, and provides a concrete example with a renegotiation trigger. It does not go into rate limits or data updates, but for a read-only benchmark tool this is sufficient.

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: the first sentence gives the use case, the second states the core functionality, and the third provides a concrete example and data source. Every sentence adds value, and it is front-loaded with the most important information.

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?

Given there is no output schema, the description adequately explains what the tool returns (benchmark cost per bed) and the optional gap analysis. It includes a real-world example and cites the data source, which gives a complete picture for a benchmark tool. It could be slightly more explicit about the required vendor parameter, but overall it is sufficiently complete.

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?

The schema has 0% description coverage, so the description must compensate. It mentions 'actual cost and bed count' which maps to annual_cost and bed_count, and the example uses 'Epic' to hint at vendor_name. However, it does not explicitly define each parameter name, type, or format, leaving some ambiguity for an agent.

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's function: 'Returns benchmark cost per licensed bed with optional gap analysis.' It uses a specific verb ('Returns') and resource ('benchmark cost per licensed bed'), and the use case ('benchmarking EHR maintenance costs') distinguishes it from sibling benchmarks like get_asc_benchmark or get_gpo_contract_benchmark.

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 explicit usage context: 'Use when benchmarking EHR maintenance costs before a contract renewal or evaluating health IT budget efficiency.' It also explains when the optional gap analysis applies ('when actual cost and bed count are provided'). However, it does not explicitly name alternative tools to use instead, so it stops short of a full 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources