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

No annotations provided, so description carries full burden. Discloses critical behavioral traits: data provenance ('proprietary multi-source modeling'), update frequency ('Updated daily'), and output structure (probabilities for escalation/ceasefire/regime change across 1/7/30-day horizons). Lacks operational details like rate limits or caching, but covers data freshness and scope thoroughly.

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 well-structured sentences: (1) scope and regions, (2) data content and methodology, (3) usage guidance. No wasted words; every clause provides necessary information. Front-loaded with the core action 'Get current geopolitical conflict risk probabilities' immediately establishing purpose.

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?

Despite lacking an output schema, the description comprehensively details the return data: specific event types (escalation, ceasefire, regime change), time horizons (1/7/30-day), and scope (6 regions). With only one optional parameter fully documented in schema, the description provides sufficient completeness for an agent to predict both input requirements and output structure.

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?

Input schema has 100% description coverage with complete enum documentation. Description lists the 6 regions (Russia-Ukraine, etc.) which maps to enum values, but this largely repeats the schema's content. With schema coverage already comprehensive, baseline 3 is appropriate as description adds minimal semantic value beyond parameter mapping.

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?

Description uses specific verb 'Get' with clear resource 'geopolitical conflict risk probabilities' and enumerates all 6 supported regions. It clearly distinguishes from sibling tools: 'get_maritime_traffic' (shipping) and 'get_political_events' (general politics) by specifying probabilistic conflict forecasting for specific military/political flashpoints.

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 the use case: 'Use this to assess geopolitical risk exposure for trading or risk management.' This provides clear context for when to invoke the tool. Does not explicitly name sibling alternatives, but the domain specificity (conflict probabilities vs. maritime traffic) makes differentiation clear without negative guidance.

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?

No annotations provided, so description carries full burden. Excellently discloses data source (AIS receivers), temporal behavior (snapshot vs. throughput), data quality limitations (military vessels underestimate counts), and update cadence (daily). Provides critical context about data reliability that annotations would typically cover.

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?

Six distinct sentences each serving unique purpose: purpose/scope, data breakdown, source clarification, limitation warning, update frequency, and use case. No redundancy or filler; information density is high throughout.

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?

Lacks output schema but description compensates well by detailing return contents (vessel counts with type breakdown) and data characteristics. Missing only structural format details, but sufficiently describes the payload for effective use given the simple single-parameter 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 has 100% coverage for the single optional parameter, establishing baseline 3. Description adds value by enumerating specific zone examples (Strait of Hormuz, Black Sea, etc.) which helps users understand valid zone_id values without being exhaustive.

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?

Description opens with specific verb 'Get' + resource 'vessel counts' + scope 'critical maritime chokepoints' and enumerates specific zones (Strait of Hormuz, Black Sea, etc.). Clearly distinguishes from siblings get_conflict_risks and get_political_events by focusing on maritime traffic data versus political/conflict analysis.

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?

Provides explicit use case: 'Use this to monitor supply chain disruption risks.' Also implies limitations ('not full-day throughput') that guide appropriate use. Does not explicitly name sibling alternatives, but the domain distinction (maritime traffic vs conflicts/events) is clear enough for selection.

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?

With no annotations provided, the description carries the full burden. It successfully discloses data freshness ('Updated daily') and return value structure ('probability, deadline, and confidence level'), but omits operational details like error handling, rate limits, or safety characteristics.

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?

Four sentences efficiently cover purpose, event examples, return data structure, and update frequency. Every sentence contributes distinct information without 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?

Given the absence of an output schema, the description adequately compensates by detailing the return value fields (probability, deadline, confidence). For a single-parameter tool, it provides sufficient context, though explicit differentiation from conflict-risk tools would improve completeness.

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?

Although the schema has 100% coverage for the single 'category' parameter, the description adds valuable semantic context by listing example event types (elections, policy changes, economic risks) that map to the enum values (political, economic, natural_disaster), clarifying what each category encompasses.

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 retrieves 'high-impact political, economic, and natural disaster events' with specific examples (elections, policy changes). However, it does not explicitly differentiate from the sibling tool 'get_conflict_risks', which could conceptually overlap with political events.

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 no explicit guidance on when to use this tool versus siblings like 'get_conflict_risks' or 'get_maritime_traffic'. While it characterizes the data (probability estimates, daily updates), it lacks 'when-to-use' or 'when-not-to-use' criteria.

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