get_hospital_rateReturns the negotiated rate a named hospital publishes in its federally required machine-readable file for a CPT/HCPCS code, with the file's as-of date. Public hospital-transparency data; educational, not billing advice.
| Name | Required | Description | Default |
|---|---|---|---|
| cpt | Yes | CPT/HCPCS procedure code. | |
| hospital | Yes | Hospital / facility name. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, establishing a safe read operation. The description adds useful behavioral context beyond that: it returns the as-of date of the source file, identifies the source as federally required machine-readable data, and explicitly frames the output as educational rather than billing advice.
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 tightly written sentences with no filler. The main function is front-loaded, the return value is stated, and the caveat is placed at the end without distracting from the primary purpose. Every sentence earns its place.
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?
For a two-parameter read-only lookup with no output schema, this description is sufficient. It states what is returned (negotiated rate plus as-of date), clarifies the data source (federally required machine-readable file), and adds a user-facing limitation (educational, not billing advice). No critical information is missing for an agent to call the tool correctly.
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 100%, so the schema already documents both parameters. The description adds minimal extra meaning beyond what the schema provides, only restating that the tool looks up a named hospital and a CPT/HCPCS code. Baseline 3 is appropriate because the schema carries the parameter documentation burden.
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 uses a specific verb ('Returns'), names the resource ('negotiated rate a named hospital publishes'), and specifies the key inputs (hospital, CPT/HCPCS code). It also clearly distinguishes this tool from siblings like explain_denial_code or get_insurer_overturn_rate by focusing on hospital-published transparent pricing.
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?
The description provides clear context: use this tool to retrieve a hospital's published negotiated rate for a CPT/HCPCS code. It does not explicitly name alternative tools or state when not to use it, but the hospital-transparency framing and the 'educational, not billing advice' caveat make the intended use obvious.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.