Enzo's

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

The annotations already establish that this is a safe, read-only, open-world operation. The description adds useful behavioral detail beyond that: results are 'currently priced BELOW comparable homes' and ordered 'newest-best first.' It does not define 'comparable' precisely, but it is transparent enough for the annotations already provided.

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 efficient sentences, front-loaded with the primary purpose and signal definition. It contains no redundant or wasted phrasing.

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 listing tool with full parameter coverage and an output schema, the description provides enough information to invoke it correctly. Minor ambiguity remains around how 'comparable homes' and 'newest-best' are computed, which keeps it from a 5.

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%, so the schema fully documents both parameters. The description restates that the input is a metro or city name but adds no additional constraints, examples, or format details beyond what the schema 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 gives a specific verb ('List') and resource ('homes') with a clear qualifying criterion: priced below comparable homes. It also scopes results to a metro and states a default ordering, though it does not explicitly differentiate itself from the sibling search_homes tool.

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 purpose implies when to use it: finding below-market or good-value homes in a metro. However, there is no explicit guidance on when to choose this over search_homes or when not to use it, so usage is only implied.

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, openWorldHint=true, and destructiveHint=false. The description adds useful behavioral context by enumerating the breadth of returned data, including photo-detected features, fact-grounded description, video tour presence, similar homes, and source listing link.

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 purpose and input are front-loaded, and every listed data field earns its place by clarifying scope. It is a single long sentence, which is slightly unwieldy, but not padded with irrelevant material.

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?

With annotations covering safety and an output schema documenting the return shape, the description supplies all an agent needs to select and invoke the tool correctly. It also names the input format and provenance explicitly.

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% and already documents the single 'home' parameter as an enzos.ai URL or 16-character id. The description restates the same format and adds only that the id is 'as returned by search_homes', so it does not meaningfully exceed the schema.

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?

States the specific verb 'Get' and resource 'full facts for one for-sale home on Enzo's', then enumerates the exact data included. This clearly distinguishes it from search_homes, which returns multiple homes.

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?

Explains that the input is an Enzo's home URL or id 'as returned by search_homes', which makes the intended workflow clear. It does not explicitly exclude alternatives like home_verdict or below_market_deals, but the usage context is clear.

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 cover read-only/safe behavior, yet the description still adds substantial context: it says the result is deterministic and grounded in FRED, Zillow ZHVI and state tax rates, that the grade comes from a Monte-Carlo of local price dynamics versus renting with p10/p50/p90 and a probability, and that it is a probabilistic model, not a guarantee, sales pitch, or financial advice. That tells the agent what kind of output to trust and how to present it.

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 front-loaded with what the verdict returns, then the data sources and model caveats, which matches how an agent would scan it. It is a dense run-on sentence with some redundant hedging ('never a guarantee, a sales pitch, or financial advice'), but every clause carries selection value; nothing is padding.

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?

Although an output schema exists, the description still gives the agent a complete mental model: inputs accepted, data grounding, model type and variability (p10/p50/p90, probability), and explicit limitations on how the grade should be used. Nothing needed to decide whether to call it or how to interpret the result is missing.

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% and the single parameter is fully documented as an enzos.ai URL (.../home/<id>) or 16-character id. The description's closing sentence ('Input is an Enzo's home URL or its id') merely restates the schema, so it adds no new format hints or edge-case behavior.

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 ('Get Enzo's honest, independent read for one listing') and enumerates exactly what the verdict contains: price comparison, $/sqft, days on market, price cuts, monthly cost, income needed, and an A–F Monte-Carlo grade. That is enough to separate it from get_home or market_snapshot, but it never names a sibling or contrasts itself with them explicitly.

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 signals usage implicitly ('for one listing', 'Input is an Enzo's home URL or its id'), so an agent can infer this is the single-listing deep-dive tool. There is no explicit when-to-use/when-to-avoid guidance nor any pointer to below_market_deals or get_home as alternatives.

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, openWorldHint=true and destructiveHint=false, so the safety profile is covered. The description adds that the data is 'live' and scoped to US metros on Enzo's, but says nothing about behavior on ambiguous or unknown metro names, data recency, or coverage limits.

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?

A single tight sentence that front-loads what the snapshot contains, then closes with the input format. Every clause carries information; nothing is padding.

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?

With an output schema present, return values needn't be explained, yet the description still previews them helpfully, and the one required input is exemplified. The only gap is guidance on how to handle city-vs-metro ambiguity or non-US input.

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% and the single parameter already carries its own description and examples, so the description merely restates the same 'Austin, TX' style example. With one fully documented parameter, the schema does the heavy lifting and no additional semantic value is added.

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 resource (a US metro's for-sale market snapshot) plus the exact metrics returned — inventory count, 5th/median/95th percentile price, and common architectural styles. It never names a sibling (e.g. below_market_deals also concerns market pricing), so the agent must infer the boundary from names alone.

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?

Usage is implied: use it when you need aggregate market-level stats for a metro rather than individual listings. There is no explicit when-not guidance and no pointer to below_market_deals or search_homes as the alternatives for listing-level queries.

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 and destructiveHint=false, so safety is covered; the description adds what annotations cannot — that results are real for-sale listings sourced from Enzo's with price, style, and an outbound link to the original listing. It does not state result cap, ranking, or pagination behavior, though the schema limit and the output schema partially cover that ground.

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?

Three sentences, front-loaded with the capability before the return payload and then the examples. Slight redundancy exists between the description's 'tudor homes under $1M in Pasadena' example and the identical example in the schema, which is the only wasted space.

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 two-parameter read tool with full schema coverage, complete annotations, and an output schema, the description covers capability, query expressiveness, expected input shape, and return payload. Nothing an agent needs in order to call this correctly is missing.

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%, so the baseline is 3, but the description genuinely extends semantics by spelling out the facets a free-text query can carry (architectural style, features, budget, beds/baths, metro or neighborhood) — far richer than the schema's single example string. It also implicitly frames limit as results-per-query rather than a filter.

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 gives a specific verb+resource ('Search real for-sale homes on Enzo's') and enumerates exactly which facets are searchable — style, features, budget, beds/baths, location. It stops short of naming a sibling, so an agent cannot tell from the text alone why it should pick this over below_market_deals or get_home.

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?

Two concrete example queries ('spanish revival homes with a pool under $1.5M in Los Angeles', 'mid-century modern in Palm Springs') clearly establish the invocation context as broad exploratory discovery. No exclusions or alternative-routing guidance are given, so the agent must infer that get_home handles single-property lookups.

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 readOnly, non-destructive, open-world, and an output schema exists, so the safety profile is covered. The description does add real value by disclosing provenance and a coverage limitation — the tours are agent-filmed TikTok content 'which the big listing portals do not carry' — implying sparse, non-exhaustive results. It stops short of stating result ordering, freshness, or what happens when no matches exist.

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, zero waste, with the distinguishing scope front-loaded before the optional filter. Nothing is padded or repeated from the schema.

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 zero-required-parameter read tool with full schema coverage, an output schema, and complete annotations, the description is nearly sufficient. A note on what an empty result means (many metros likely have no TikTok tours) would close the remaining gap.

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 both parameters (limit with its 1-25 range and default, metro/city/state) are already fully documented. The description restates the metro filter without adding syntax or matching behavior beyond the schema's example, so the baseline 3 applies.

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?

States a specific verb ('List') and a precisely scoped resource ('for-sale homes that have a real agent-filmed video walkthrough'), which is a genuinely different result set from search_homes. It doesn't name the sibling it supersedes, but the scope qualifier alone lets an agent tell them apart.

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?

'Optionally filter to a metro or state' implies the usage context, but there is no explicit when-to-use vs. when-not, and no pointer to search_homes for the general case or to below_market_deals for deal filtering. The agent must infer selection from the niche scope.

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