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

No annotations provided, so the description carries full burden. It explains pattern types but does not disclose potential behavioral traits like read-only nature, rate limits, or error handling. Basic transparency is achieved.

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 a single sentence followed by a list of pattern types, front-loading the main purpose. It is fairly concise, though the pattern explanations could be slightly more streamlined.

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 has 6 parameters and no output schema, the description does not explain return values or behavior for edge cases. It covers pattern selection but omits output details, leaving some gaps.

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% with descriptions for all parameters. The description adds value by elaborating on the pattern_type enum values, explaining what each pattern means beyond the schema's simple description.

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 detects patterns across many parliamentary votes and enumerates three specific pattern types with brief explanations. It distinguishes itself from siblings which focus on entities, events, querying, and ranking.

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 states 'Use for trend analysis across many divisions,' providing clear context. However, it does not explicitly mention when not to use or direct to alternative tools, though siblings imply different use cases.

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 full burden. It clearly describes the search/fetch behavior, including filtering and parameter combinations. However, it does not mention pagination behavior, rate limits, or authentication requirements, which could be relevant for agent planning.

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 dense and informative, front-loading the main purpose. However, the structure is a single prose paragraph; a more structured format (e.g., bullet points or separate sections) could improve scanability. Nevertheless, every sentence adds value.

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 tool with 11 parameters, 4 entity types, and no output schema, the description is remarkably complete. It covers all entity_type behaviors, parameter combinations, and special use cases (e.g., interest topic queries). The agent can confidently select and invoke the tool without needing external documentation.

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?

Despite 100% schema coverage, the description adds significant value beyond the schema. It explains how entity_type changes the semantics of other parameters, provides concrete examples (e.g., name='John McDonnell', mp_id=178, keyword='property'), and details special usage patterns (e.g., finding all MPs with a topic). This goes well beyond the schema descriptions.

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 starts with a clear verb+resource statement: 'Find MPs, bills, petitions, or declared financial interests.' It then provides specific breakdown per entity_type, distinguishing each use case. The tool's purpose is explicit and distinct from sibling tools like analyze_patterns or query_entities.

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 offers extensive guidance: when to use name vs mp_id, how to combine parameters per entity_type, and special cases like fetching all MPs with an interest topic by omitting name/mp_id. It effectively tells the agent when and how to invoke the tool.

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, the description carries the burden and explains what each event_type returns (e.g., pass/fail results, rebellions, bill stages, MP voting history). It does not cover performance, rate limits, side effects, or error conditions.

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 a single paragraph but well-organized by event_type, each with a brief explanation. It is concise yet informative, though some repetition could be trimmed.

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 tool with 9 parameters and no output schema, the description adequately covers the main use cases for each event_type. However, it lacks details on error handling, pagination, or rate limits.

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?

The input schema has 100% coverage, but the description adds context beyond schema by explaining how to use parameters for each event_type, clarifying defaults (days=30, limit=20, house='Commons'), and providing usage examples.

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 retrieves recent parliamentary events and lists four specific event types (divisions, rebellions, bills, member votes) with examples, distinguishing between them effectively.

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 clear guidance on when to use each event_type, including example parameters like 'name='Nigel Farage'' for member_votes. However, it does not mention when not to use this tool or suggest alternative tools.

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, the description carries the full burden. It explains the filtering logic (intersection) and provides examples. However, it does not disclose pagination behavior, default limits (though schema notes limit), error responses, or any side effects. The description is adequate but not exhaustive.

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 a clear front-loaded statement of purpose followed by examples and usage details. Every sentence adds value, and there is no redundancy or unnecessary text.

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 complexity (7 parameters, no required, cross-dataset) and absence of output schema, the description covers the main functionality well. It explains the intersection logic and key filters. However, it omits mention of parameters like house, party, and limit, which are in the schema but not reinforced in the description. Additionally, no return format is specified, which could be helpful.

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%, but the description adds value by explaining how to combine parameters (e.g., division_id with voted or rebellion_only, and has_interest for interest filter). It also provides examples that illustrate parameter usage, exceeding the schema 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 tool's purpose as a cross-dataset query to find MPs matching multiple conditions across vote records and financial interests. It provides concrete examples and distinguishes itself from sibling tools like find_entities and analyze_patterns by emphasizing the intersection of conditions.

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 guidance on how to use the tool, including specifying division_id with voted or rebellion_only for vote filtering, and has_interest for interest filtering. It also explains that results are the intersection of all conditions. However, it does not explicitly contrast with sibling tools or specify when not to use this tool.

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 are provided, so the description must carry the full burden. It discloses that the tool scans all divisions internally and returns a sorted leaderboard, and mentions default date range. It lacks details on potential side effects or permissions, but for a read-like ranking operation, this is adequate.

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 four sentences that front-load the core action and provide necessary details. Every sentence adds information without 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?

Given the absence of an output schema, the description covers the tool's behavior well: internal scanning, defaults, and filtering. It lacks explicit mention of the return structure (e.g., 'returns list of MPs with rebellion counts'), but for a leaderboard tool, the context is largely sufficient.

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?

With 100% schema coverage, the baseline is 3. The description adds value by providing examples (e.g., 'filter by party='Labour'') and clarifying that metrics and entity types are fixed, which reinforces the schema's enum constraints. This lifts the score to 4.

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 ranks MPs by rebellion count, specifies the metric (rebellions), and gives concrete usage examples. It distinguishes itself from siblings by promising a sorted leaderboard in a single call, making the purpose unambiguous.

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 lists example queries ('most rebellious MPs', 'rebel count', etc.) and provides filter guidance (party='Labour'). However, it does not mention when to avoid this tool or suggest alternative tools, which would have made it a 5.

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