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

With no annotations provided, the description carries full responsibility. It discloses key behavioral traits: it returns a framework, does not perform the analysis, and scopes output to the situation. It does not mention side effects or permissions, but for a read/transform tool this is acceptable. The absence of contradiction with annotations (none) supports this.

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, followed by a clarifying contrast. Every word earns its place, and it avoids redundancy with 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?

The tool is conceptually simple with three parameters and no output schema. The description explains what the tool returns and what it does not do, which is essential for correct usage. It could elaborate on terms like 'ASEKE' or 'output discipline,' but given the sibling context and clear boundaries, the coverage is 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?

Schema description coverage is 100%, so the schema already explains all three parameters. The description adds context about 'behavior, situation, or conflict' aligning with the 'description' parameter, but does not provide additional semantic detail beyond what the schema offers. 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 clearly identifies the tool's purpose with a specific verb ('Scaffold') and resource ('an ASEKE analysis'). It explicitly states what it returns (5-step framework scoped to candidate patterns) and distinguishes itself from siblings by noting it does not perform the analysis itself, whereas siblings like match_patterns might.

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 clearly states a 'when not to use' boundary: 'Does not perform the analysis — the caller does.' This implies the tool is for scaffolding/preparatory work. However, it does not explicitly name alternative tools or provide a comprehensive when-to-use guide, though the sibling list and this boundary make usage 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?

With no annotations provided, the description carries the transparency burden. It discloses that results are 'probable' tendencies with caveats about timescale and IS dependence, indicating limitations. It does not explicitly state whether the operation is read-only or has side effects, but for an analytical inference tool this is implied. The added caveat about timescale and IS dependence is valuable 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 two concise sentences, front-loaded with the core purpose and followed by a relevant technical note. Every word contributes meaning; no filler or repetition.

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 no output schema and no annotations, the description adequately covers the tool's purpose, model basis, and limitations. It does not describe the exact return structure, but the mention of 'DPM tendencies' is sufficient for an inference tool. It could be more complete by adding a note about expected output format or typical use cases, but it is not severely lacking.

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 covers 100% of parameters, so baseline is 3. The description adds meaning by linking the bridge to specific parameters (FEAR to RWA, CARE breadth to SDO) and explicitly referencing 'timescale' as a contextual variable. This enriches the bare schema enums with theoretical context.

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 a specific action ('assess probable political orientation tendencies') and a distinct resource/context (Dual Process Model bridge from emotional systems). It differentiates from sibling tools by focusing on political orientation via DPM, a unique angle not present in get_system_info, analyze_behavior, etc.

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 clear context on when to use the tool (when active primary emotional systems are known and political orientation tendencies are desired). However, it does not explicitly mention when not to use it or name alternative tools, so it stops short of a full 5.

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 carries the full burden. It implicitly indicates a read-only operation ('Get') and lists the type of information returned. However, it does not disclose error handling, return format, or any prerequisites beyond naming a pattern. For a non-destructive get, this is adequate but not comprehensive.

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, front-loaded sentence that specifies the action, target, and key content areas. Every word contributes meaning, with no redundant or vague 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?

With no output schema, the description should convey what the tool returns. It lists specific categories of information, giving a solid overview. However, it does not describe the response structure or potential edge cases, which for a simple lookup is a minor gap. Overall, it is complete enough for the tool's simplicity.

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 the parameter description 'Pattern name' and an enum. The description adds 'named behavioral pattern' which reinforces the parameter's role but does not provide new syntactic or format details. Since the schema already fully documents the parameter, 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 clearly states the tool's function: 'Get detailed information about a named behavioral pattern' with specific content categories (substrate, recognition signals, blind spots, leverage points). This distinguishes it from siblings like get_system_info or list_all, which serve different purposes.

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 context is clear: use this when you have a specific named behavioral pattern and need details. It does not explicitly mention alternatives or exclusions, but the singular 'named' pattern implies it is for a specific pattern rather than a list or broad analysis, providing sufficient 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?

With no annotations, the description carries the burden of disclosing behavior. It implies a read-only operation via 'Get' and lists what information is returned, but it does not explicitly state that it has no side effects, requires no permissions, or describe any other behavioral traits. This is a moderate level of transparency.

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, front-loaded sentence that efficiently lists the tool's scope and output fields without any wasted words. It is appropriately sized for the tool's simplicity.

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 one-parameter getter with no output schema, the description clearly enumerates what information will be returned. It is complete for most uses, though it could optionally explain domain-specific terms like 'CE demand' or 'IS vulnerability profile' for less familiar agents.

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 fully documents the single parameter 'system' with an enum and description. The description adds no additional parameter semantics beyond what is already in the schema, so the baseline score of 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?

The description clearly states the tool's verb (Get) and resource (detailed information about a Panksepp primary emotional system), and lists specific content areas (neural basis, behavioral signature, CE demand, IS vulnerability profile, political bridge). This distinguishes it from sibling tools like get_pattern_info and list_all.

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 implies usage when one needs information about a specific emotional system, but it does not explicitly state when to use this tool versus alternatives like bridge_to_political or analyze_behavior. No exclusions or alternative recommendations are provided.

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 explicitly describes a read-only listing operation with no side effects, and clarifies that it returns all items, not a filtered subset. It does not disclose output format, but for a zero-parameter list tool 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 a single, focused sentence that front-loads the action and provides essential scope information without any fluff.

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 enumeration tool with no parameters and no output schema, the description fully covers what an agent needs to know: the resource type, scope, and framework context.

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 tool has zero parameters, so the baseline is 4. The description reinforces the no-filter nature by saying 'all available', which aligns with the empty input 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?

The description clearly specifies the action ('List'), the resource ('all available primary emotional systems and named behavioral patterns'), and the context ('ASEKE framework'). It distinguishes itself from sibling tools that fetch specific system or pattern info.

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 scope ('all available') implies this is the enumeration tool, but the description does not explicitly state when to use it over alternatives like get_system_info or get_pattern_info, nor does it provide exclusion criteria.

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 properly carries the burden of disclosing behavior. It explicitly states this is token overlap, not comprehension, and that results are a shortlist to verify, never a verdict—key limitations that prevent misuse. This is strong behavioral transparency.

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 long, with the primary purpose in the first sentence and a crucial caveat in the second. Every word earns its place; no filler or redundancy. Perfectly structured for quick consumption.

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 covers what the tool does, what input it expects, what output to expect (candidate patterns with recognition text), and a critical limitation (not a verdict). No output schema exists, so this explanation is necessary and sufficient. Sibling tool names add context but the description alone is complete for a prefilter 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 description coverage is 100%, so the schema already documents both parameters (signals and limit). The description adds context for 'signals' by calling them 'observed behavioural signals' and giving an example query format, but it does not elaborate on 'limit' or return details beyond what the schema provides. This meets the baseline for high schema coverage.

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 is a lexical prefilter over the pattern library, using specific verbs like 'returns' and identifying the resource ('pattern library'). It distinguishes from siblings by emphasizing token overlap rather than comprehension, and the shortlist/verdict caution differentiates it from analytical tools like analyze_behavior.

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 context for use: 'Given observed behavioural signals' and 'treat results as a shortlist to verify, never as a verdict' give practical guidance. It does not explicitly name alternative tools or state when not to use it, but the implied usage is clear enough.

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