Get Fonteum API and MCP methodology, provenance-field definitions, generated source catalog, and separately dated platform coverage. Field population varies by source and response. Use for interpreting Fonteum outputs, citation fields, dataset limitations, or integration planning.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already signals a safe read operation. The description adds valuable caveats: 'Field population varies by source and response' and 'separately dated platform coverage,' which inform users about data completeness and temporal variability.
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?
Three concise, purposeful sentences. The main function is front-loaded, followed by a caveat and usage guidance. No fluff or redundant phrasing.
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 zero-parameter metadata tool, the description covers what is returned (methodology, provenance fields, source catalog, coverage dates), usage scenarios, and a limitation. It lacks an explicit mention of output format, but the content list compensates reasonably.
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?
The tool has no parameters, so the baseline of 4 applies. The description correctly avoids inventing parameter details and instead focuses on the content returned, which is appropriate.
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 clearly states the tool retrieves Fonteum API/MCP methodology, provenance-field definitions, source catalog, and platform coverage. This distinguishes it from data-lookup siblings like fonteum_lookup_npi or fonteum_search_providers, though it could be more pointed about being a metadata-only endpoint.
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?
Explicitly states usage context: 'Use for interpreting Fonteum outputs, citation fields, dataset limitations, or integration planning.' It does not name alternatives or give when-not-to-use guidance, but the intended scenarios are clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes, but there is potential confusion between check_exclusion, search_sanctions, and lookup_npi since all touch exclusion data. However, descriptions clarify the entry point (NPI vs name) and specific sources, making misselection unlikely but possible for agents unfamiliar with the domain.
Every tool follows the consistent prefix_verb_noun pattern (e.g., fonteum_lookup_npi, fonteum_search_providers). The verbs are varied but appropriate to the action, and the naming convention is uniform throughout.
With 9 tools, the server is well-scoped for a healthcare provider data and compliance lookup service. Each tool covers a distinct operation, and the count is within the ideal range for such a domain.
The surface covers core workflows: provider lookup, search, exclusion checking, sanctions screening, contractor lookup/history, dataset info, and snapshot verification. Minor gaps exist such as no historical NPI lookup or batch search, but these are not critical for the server's purpose.
