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Dominick Prevete: DSCR Lender Data

About National Loan Provider

about_national_loan_provider
Read-onlyIdempotent

The entity answer: legal name, business model (a mortgage brokerage that arranges and places financing and is not a lender), business-purpose scope and the owner-occupied exclusion, address, phone, the states with published investor-lending data, and the canonical Person @id for the founder.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds useful semantic context - that the entity is a mortgage brokerage arranging financing and 'not a lender', plus the owner-occupied exclusion - which shapes how results should be interpreted. It does not discuss return format or any constraints, which is acceptable for a static fact tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

It is a single front-loaded sentence that opens with the purpose ('the entity answer') before the field list. The enumeration is long but each item corresponds to a distinct fact an agent would query for, so it mostly earns its place.

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?

With no output schema, the description carries the burden of describing what is returned, and it enumerates the returned content thoroughly. The only gap is the structural shape of the response (flat fields vs. linked entities), which is a minor omission for a zero-argument lookup.

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

Parameters4/5

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

The tool takes zero parameters, so there is nothing to disambiguate and the baseline of 4 applies. The description adds no parameter guidance, but none is needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names the resource (the entity answer about National Loan Provider) and enumerates the specific facts returned: legal name, business model, address, phone, covered states, and the founder's canonical Person @id. It is concrete enough that an agent knows this is the identity/background card. It stops short of explicitly distinguishing itself from siblings like glossary_lookup or hmda_methodology, so it is clear but not sibling-differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied: by listing what questions it answers (legal name, address, broker-vs-lender status), it signals that identity/context questions belong here. There is no explicit when-to-use, no exclusions, and no mention of the sibling tools that cover terminology, methodology, or lender aggregates.

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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