Hosted MCP server exposing US hospital procedure cost data to AI assistants
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 3.4/5 across 2 of 2 tools scored.
The two tools have distinct purposes: one lists hospitals with metadata, the other retrieves cost data for a specific hospital. There is no overlap.
Both tools follow a consistent verb_noun snake_case pattern: list_hospitals and get_hospital_chargemaster_cost.
Two tools is minimal for a medical pricing API; while it covers listing and retrieval, the scope feels limited. It is borderline but not extreme.
The API lacks ability to search by procedure, compare costs, or filter hospitals. Only basic listing and single cost lookup are provided, which are significant gaps for typical use cases.
get_hospital_chargemaster_costGet hospital chargemaster costCLookup hospital chargemaster cost
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | ||
| code_type | Yes | Code system the chargemaster/billing code belongs to, e.g. APR-DRG, CDM, CPT, HCPCS, MS-DRG, RC. Hospitals may also support additional proprietary code types not listed here. | |
| hospital_id | Yes | Opaque hospital identifier from list_hospitals. | |
| methodology | No | Pricing methodology. Omit to aggregate across all methodologies. |
| Name | Required | Description |
|---|---|---|
| cost | No | |
| found | No | Whether a matching chargemaster cost record was found. |
| hospital | No | Hospital name returned by the MedPrice AI backend. |
| description | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the description adds no behavioral context. It does not mention return format, error handling, or any side effects. With annotations, the description should add value beyond them, which it fails to do.
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?
The description is excessively brief, consisting of only three words. It is under-specified for a tool with 4 parameters and an output schema, making it inadequate rather than concise.
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?
Given the tool's complexity (4 parameters, output schema), the description fails to explain what the tool returns, how it works, or when it is appropriate to call. It is insufficient for an agent to use correctly without additional inference.
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 75% (3 of 4 parameters have schema descriptions). The tool description provides no additional parameter context, so it does not compensate for the missing parameter description nor add value over the schema.
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 states the verb-resource combination 'Lookup hospital chargemaster cost' which indicates the core action, but it lacks specificity about what is looked up (e.g., by code, hospital) and does not distinguish from sibling tool list_hospitals, though differentiation is minimal here.
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?
No guidance on when to use this tool versus alternatives, no prerequisites or exclusions. The description is silent on context, leaving the agent to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_hospitalsList supported hospitalsAReturns the hospitals supported by the medprice.ai API, with their hospital_id (opaque DB key), EIN, name, locations, last_updated_on, and revision history (with per-revision has_payer_data). Supports pagination.
| Name | Required | Description | Default |
|---|---|---|---|
| page_size | No | Maximum number of hospitals to return. Defaults to 20, capped at 100. | |
| page_token | No | Opaque token from a previous list_hospitals response. Omit for the first page. |
| Name | Required | Description |
|---|---|---|
| hospitals | No | |
| next_page_token | No | Opaque pagination token, empty when there are no more results. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true and destructiveHint=false, which the description aligns with (returns data, no side effects). Description adds pagination behavior and list of returned fields.
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 concise sentences. First sentence states purpose and key fields; second covers pagination. No unnecessary words.
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?
Given low complexity, presence of output schema, and annotations, the description provides complete context for agent invocation. No gaps.
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 coverage is 100% for both parameters. Description adds default page_size (20, capped at 100) and clarifies that page_token is opaque from previous response, going beyond schema.
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?
Clearly states it returns hospitals supported by the API, listing key fields (hospital_id, EIN, name, etc.) and pagination. Differentiates from sibling tool get_hospital_chargemaster_cost.
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?
Mentions pagination and parameter usage (omit page_token for first page), but does not explicitly state when not to use or contrast with sibling tool.
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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{
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Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
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