Zero-auth Streamable HTTP MCP endpoint serving 45 property-type MACRS 5-year, 15-year, and 27.5/39-year cost segregation depreciation allocation bands from Modern CFO (https://freecostseg.com/resources/cost-segregation-mcp/) and Overline IQ (https://overlineiq.com/cost-segregation-tax-savings).
Scored across 1 tool
With only one tool there is no possibility of selecting the wrong one; its purpose (looking up a cost segregation allocation band by property type) is unambiguous. The description also clearly scopes what the tool does not return (tax advice, empirical percentiles).
The single name follows a clear, descriptive verb_noun pattern (lookup_cost_segregation_benchmark) in snake_case and matches the server's domain vocabulary. There are no competing conventions to conflict with.
The server's stated purpose is narrow (one benchmark lookup), so a single tool is defensible rather than absurd. However, a one-tool surface is thin — no companion tool to enumerate supported property types or available bands — making it borderline for practical agent use.
The core lookup operation exists, but the surface is a dead end for discovery: an agent cannot list which property types or bands are supported, nor inspect provenance separately before querying. These are workable gaps but likely to cause trial-and-error lookups.
Look up a public, expert-maintained cost segregation calculator allocation band by property type. Returns planning assumptions, provenance, and limitations, not tax advice or empirical percentiles.
| Name | Required | Description | Default |
|---|---|---|---|
| property_type | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=false, and destructiveHint=false, so the safety profile is clear. The description adds valuable context beyond annotations by disclosing that it returns 'planning assumptions, provenance, and limitations' and explicitly excludes tax advice and empirical percentiles, setting correct expectations for the output.
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 wasted words. The core action and subject are front-loaded, and the second sentence efficiently covers output nature and limitations.
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 read-only lookup with one enum parameter and no output schema, the description supplies enough context about what the tool returns and what it does not return. It could be slightly more specific about the format of the allocation band, but it is otherwise complete for an agent to invoke 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?
The schema has 0% description coverage, but the single parameter is an exhaustive enum of property types with self-explanatory values. The description mentions 'by property type,' aligning with the parameter, but adds no further semantic detail about value selection or formatting beyond what the enum already provides.
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 a specific verb and resource: 'Look up' an 'allocation band by property type' from a 'cost segregation calculator.' It also clarifies the nature of the source as 'public, expert-maintained,' so the agent knows exactly what operation is being performed and on what kind of data.
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 implies usage by property type and notes that the result is 'not tax advice or empirical percentiles,' which frames interpretation. However, it does not explicitly say when to use this tool versus alternatives or provide any prerequisites, so usage guidance is only implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
lookup_cost_segregation_benchmark
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