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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows it's a safe, read-only operation. The description adds no extra behavioral context (e.g., side effects, auth requirements, rate limits), relying on the annotations to carry the safety profile.

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, front-loaded with the purpose and outputs, followed by usage context. Every sentence earns its place; no unnecessary words.

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 there is no output schema, the description lists all key return metrics and gives usage context. It doesn't explain edge cases, but since the annotations confirm read-only/idempotent behavior and the schema covers parameters, it is fairly complete for a straightforward analysis 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% with every parameter having a description, including defaults for assumptions and the fallback for monthly_expenses. The description adds little beyond listing outputs; it does not elaborate on parameter syntax or constraints, so the schema does the heavy lifting.

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 analyzes multifamily deals, lists specific outputs (cap rate, cash-on-cash, DSCR, IRR, DFP Score, max offer price, full financial projections), and includes usage context. This distinguishes it from siblings like score_deal and reverse_calc through the breadth of outputs.

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 when to use it: 'when someone asks about analyzing a property, evaluating a deal, or running the numbers.' However, it does not provide exclusions or mention alternatives like score_deal or market_data, so it's clear on triggers but not on when NOT to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=true, and idempotentHint=false, covering the safety and world-awareness profile. The description adds the specific return fields (flood zone, income relative to state median, job growth rate), which is useful context beyond annotations. However, it does not describe any additional behavioral nuances such as data freshness, rate limits, or edge cases (e.g., missing data for some addresses). Given the annotations handle the core safety traits, a score of 3 is appropriate.

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 two sentences with zero wasted words. It leads with the primary action ('Look up market intelligence for a property address'), then lists the specific returned data, and finally gives usage guidance. Each sentence earns its place; there is no redundancy or filler.

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 simple read-only lookup with no output schema, the description adequately covers what the tool does, what it returns, and when to use it. The parameter schema fully documents inputs. However, it doesn't mention potential caveats such as data availability for certain addresses or any limitations on geographic coverage. While not critical, this leaves a small gap in completeness for an agent that might need to handle edge cases. A score of 4 reflects this minor omission.

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 description coverage is 100% — both 'address' and 'zip' have descriptive text in the schema (address includes full property address example, zip mentions auto-extraction). The description does not add any parameter-specific semantics beyond what the schema already provides. Baseline 3 is correct when the schema carries the full parameter documentation.

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 verb 'look up' and the resource 'market intelligence for a property address', and explicitly lists the specific data returned (flood zone, neighborhood income relative to state median, job growth rate). This distinguishes it from sibling tools like analyze_deal, score_deal, and reverse_calc, which focus on deal analysis and calculations rather than property market 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 provides explicit usage guidance: 'Use this when someone asks about a market, neighborhood, or location.' This gives a clear trigger for when to invoke the tool. It does not explicitly mention alternatives or when not to use it, but the context of siblings (deal analysis, scoring, reverse calc) makes the distinction implicit. A clear usage trigger without exclusions earns a 4.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds that it back-solves from metrics, but doesn't detail any side effects, prerequisites, or limitations beyond that. This adds some context but not substantial behavioral depth beyond annotations.

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 concise, with two sentences that are perfectly front-loaded. The first sentence states the core purpose, and the second gives clear usage triggers. No wasted words.

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?

The tool has nested objects and multiple parameters, but the description gives an adequate overview of its purpose and usage. It doesn't explain how assumptions work or the fact that multiple targets can be provided, but the schema descriptions cover these. Since there is no output schema, the description implies the return (max price) clearly enough. Overall it is complete for most agent needs.

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 description coverage is 100%, so the baseline is 3. The description mentions target return metrics and back-solving but doesn't elaborate on individual parameters. The schema itself provides detailed descriptions for each parameter, so the description doesn't add extra meaning beyond what the schema already covers.

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 calculates the maximum offer price for a multifamily deal based on target return metrics, using specific verbs and resources. It differentiates itself from sibling tools (analyze, score, market data) by focusing on pricing and explicitly invokes 'what should I offer?' which is a distinct use case.

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?

It provides explicit usage guidance: 'Use this when someone asks ...' which clearly indicates the trigger conditions. However, it does not mention when not to use it or mention alternative tools, so it lacks exclusions and explicit comparisons.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already provide readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is well covered. The description adds value by specifying the return value (DFP Score and key metrics) and the 'quick' performance characteristic, which goes beyond the annotations.

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 two sentences, front-loaded with the core purpose and return value, followed by usage guidance. Every sentence earns its place, with no filler or redundancy.

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?

The description communicates what the tool does, when to use it, and what it returns. The phrase 'key metrics' is somewhat vague, but the schema and annotations fill in the rest. For a quick-screening tool, this is sufficient for an agent to invoke it 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?

All 5 parameters have descriptions in the input schema, so coverage is 100%. The tool description does not add any parameter-specific details beyond what the schema provides, so the baseline 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 states a specific verb ('score'), resource ('multifamily deal'), and scale ('DFP 0-100'), and distinguishes itself from full analysis, which maps to the sibling analyze_deal. The mention of 'key metrics' adds scope, making it clear what the tool does.

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?

It explicitly says 'use this for quick screening' and notes it is 'faster than full analysis', which implies an alternative for deeper evaluation. The sibling tools are not named directly, but the context signals list them, so an agent can infer when to choose this tool over analyze_deal.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.