Analyze a parsed rent roll for investment risks. Feed the output from analyze_rent_roll directly into this tool. Returns: rollover risk, tenant concentration, credit risk, and actionable recommendations.
| Name | Required | Description | Default |
|---|---|---|---|
| rent_roll_json | Yes | JSON string from the analyze_rent_roll tool output |
| 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 full burden. It discloses the tool analyzes risks and returns structured output, implying a read-only operation. However, it does not detail potential side effects, permissions, or whether the analysis is deterministic or involves external calls. This is adequate but not thorough.
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?
The description is three sentences long, front-loaded with purpose, followed by a usage instruction, then output summary. Every sentence adds value with no redundancy or fluff.
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?
Given a single simple parameter, high schema coverage, and an output schema (assumed to exist), the description adequately covers input source and output contents. No additional context is needed for correct tool invocation.
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?
There is one parameter (rent_roll_json) with schema description coverage at 100%. The description in the tool repeats that it expects JSON from analyze_rent_roll, which matches the schema. No additional semantics beyond the schema are provided, so a baseline score of 3 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 it analyzes a parsed rent roll for investment risks, and explicitly names the expected input source (analyze_rent_roll output). It lists specific risk categories returned (rollover, concentration, credit, recommendations), distinguishing it from sibling tools like analyze_rent_roll which parses, and others like build_dcf_model or generate_deal_memo.
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 explicitly instructs the agent to feed the output from analyze_rent_roll directly into this tool, providing clear guidance on when to use it. It does not mention when not to use or list alternatives, but the context is sufficient for correct invocation.
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.