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

Annotations indicate readOnlyHint=true and openWorldHint=true. The description adds that it returns a structured, audited deliverable and mentions async behavior (returns job_id for slow processing). It also notes server-side validation. These details go beyond the annotations and are consistent with them, providing useful behavioral context.

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 lengthy with multiple example queries and a detailed reference case. While it front-loads the purpose, it becomes wordy. Some information could be condensed without losing clarity. The structure flows from general purpose to examples, but the length impacts conciseness.

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 the tool's complexity (9 parameters, no output schema), the description covers the main use cases, outputs, and data sources (DVF, Géorisques). It mentions the deliverable includes cap rate, location score, risk flags, and recommendation. The async behavior is explained. However, it does not detail optional parameters or parameter interactions, leaving some gaps for complete usage understanding.

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 only 11%, meaning most parameters lack schema-level descriptions. The tool description does not systematically explain each parameter's meaning or format. It gives example queries that imply mappings (e.g., 'price' to asking_price_eur, 'keys' to units_or_keys), but these are not explicit. For a tool with 9 parameters, this leaves ambiguity.

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 is a real estate deal screener for the EU, specifically using French DVF and Géorisques data. It enumerates the output: cap rate vs market, location score, risk flags, and deal recommendation. Example queries demonstrate its scope (hotel, commercial, due diligence). It distinguishes from sibling tools like 'ma_deal_screener' and 'real_estate_intel' by focus on EU real estate with specific data sources.

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 multiple example queries that illustrate when to use the tool: screening deals, evaluating investments, running due diligence, assessing commercial real estate. It references a case study. However, it does not explicitly state when not to use it or contrast with alternatives. The context is clear but lacks exclusions.

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