Extract structured tenant and lease data from a rent roll document. Paste the text content of your rent roll PDF here (copy-paste from PDF reader). Returns tenant list, suite/SF, lease dates, monthly rent, escalations, and options.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Raw text copied from a rent roll PDF | |
| property_name | No | Optional property name for context |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It describes the tool as extracting structured data (safe, read-only) but does not disclose error behavior, rate limits, or what happens if the input is not a rent roll. Adequate but not rich.
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 sentences with an appended list of return values. Purpose is front-loaded. Every sentence earns its place – no filler. Very concise and structured.
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?
An output schema exists (not shown but stated), so description need not detail return format. It lists key outputs (tenant list, suite/SF, etc.) which is sufficient. Could mention input format expectations, but overall complete for a data extraction tool.
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% – both 'text' and 'property_name' have schema descriptions. The tool description adds marginal value: it reiterates the paste instruction and context for property_name. Baseline 3 is appropriate as schema already documents the parameters adequately.
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 'Extract structured tenant and lease data from a rent roll document' – a specific verb and resource. It lists extracted data (tenant list, suite/SF, lease dates, etc.), and no sibling tool duplicates this function.
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 instructs to paste text from a rent roll PDF. This tells how to use it, but does not mention when not to use it or alternatives among siblings (e.g., abstract_lease). Still, the instruction is clear and actionable.
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.
Each tool targets a distinct resource and action: lease documents, rent rolls, DCF models, Excel export, risk flags, deal memos, market data, rates, inflation, demographics at two geographic levels, and land screening at two scales. No two tools have overlapping purposes.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., abstract_lease, get_current_rates, screen_parcel_dd), making the tool surface predictable and easy to navigate.
With 13 tools, the server is well-scoped for CRE investment analysis, covering lease abstraction, rent roll analysis, DCF modeling, market data, demographics, and land screening. The count is sufficient without being bloated.
The tool set covers the full lifecycle of CRE deal analysis: document parsing (lease, rent roll), financial modeling (DCF, Excel export), risk assessment (flag risks, parcel screens), market context (rates, inflation, demographics, CRE market data), and output (deal memo). No obvious gaps for the intended purpose.