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

No annotations provided, so the description carries the full burden. It discloses the output (table of properties with differences) and lists available materials, but does not mention whether the operation is read-only, side effects, or data freshness. Adequate but not thorough.

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, no fluff. The first sentence states the core action and output, the second lists available inputs. Well front-loaded and efficient.

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 simplicity (one parameter, no output schema), the description adequately explains input, output, and available options. Sibling tools provide context (e.g., get_material_properties for single material). Slight lack of detail on return format but acceptable without output schema.

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 single parameter 'materials' is fully described in the schema (100% coverage). The tool description adds value by listing available material IDs, going beyond the schema's example. Baseline 3 is elevated due to this extra 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 the tool compares 2-4 materials side-by-side, returns a table of processing, thermal, and mechanical properties with key differences highlighted. It also lists available materials, making the purpose specific and distinct from sibling tools like get_material_properties.

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 implicitly indicates usage for comparing multiple materials but does not explicitly state when to use this tool over alternatives (e.g., get_material_properties for single material). No exclusions or clear context for when not to use it.

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?

Without annotations, the description adequately states the output format (pass/warn/fail status) and that it is generative based on part description. However, it does not disclose potential side effects, error handling, or limitations beyond injection molding.

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 two sentences, front-loading the purpose. No unnecessary words; every sentence contributes 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?

Given the complexity (11 parameters, no output schema), the description sufficiently explains the output format and that it checks 15+ design rules. It could mention error handling or return structure details, but it is reasonably complete.

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 input schema already describes each parameter. The description adds no further meaning beyond the schema, earning a baseline of 3.

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: generate a DFM checklist for injection molded parts, with pass/warn/fail status for 15+ design rules. It is distinct from sibling tools which focus on material comparison, simulation specification, 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?

No guidance is provided on when to use this tool versus alternatives or when not to use it. The description does not mention prerequisites, context, or exclusions.

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 full burden. It only mentions outputs but does not disclose behavioral traits like whether it is read-only, requires authentication, has rate limits, or if it modifies any state. This is insufficient for a tool with no annotation safety net.

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-loading the primary action and listing key outputs. Every sentence adds value 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 partially compensates by listing output categories, but doesn't specify if the output is structured as JSON or text. For a simple two-parameter tool with good schema coverage, this is adequate but not thorough.

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 description does not need to explain parameters. It adds no extra meaning beyond what the schema's descriptions already provide for 'description' and 'cad_format'.

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 that the tool generates a structured simulation specification from natural language input, listing outputs like analysis types and mesh recommendations. It distinguishes from siblings like 'compare_materials' and 'get_material_properties' which focus on data retrieval, but it doesn't explicitly contrast with the generative sibling 'generate_dfm_checklist'.

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?

No guidance is provided on when to use this tool versus alternatives such as 'generate_dfm_checklist' or 'query_simulation_knowledge'. There is no mention of prerequisites, limitations, or when not to use it.

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. Description states it 'returns' multiple property sets, implying a read operation with no side effects, but does not explicitly confirm read-only behavior, disclose rate limits, or discuss response size.

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: first defines purpose and scope, second lists allowed material identifiers. Actionable, no filler, front-loaded.

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?

Covers what the tool returns (property groups) and valid inputs (materials). Lacks output schema or return format description, but for a straightforward lookup this is acceptable. The list of materials compensates for missing enum constraints.

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 both parameters (material, properties) with descriptions. The description adds concrete list of 20 available materials and 5 property group names, enriching the schema's examples and helping the agent form valid queries.

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?

Clearly states 'Look up material properties for injection molding simulation' and enumerates specific property groups (Cross-WLF viscosity, 2-domain Tait PVT, etc.). Distinct from sibling tools like compare_materials or validate_process_parameters.

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 list of available materials, which helps choosing valid inputs, but lacks explicit when-to-use/not-use guidance or comparisons with alternatives. Use is implied but not directed.

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 fully disclose behavioral traits. It only describes purpose and topics, but fails to mention any behavioral aspects such as read-only nature, response format, potential latency, or limitations (e.g., no guarantees on result quality). This is a significant gap for a tool with no safety annotations.

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: the first states the action and scope, the second gives usage guidance. No superfluous words; every sentence provides necessary information. It is well front-loaded with the action and primary content.

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 moderate complexity (two parameters, no output schema), the description covers purpose and usage but omits expected output behavior (e.g., returns text answer, list of references) and any constraints (e.g., scope of knowledge base). While adequate for basic understanding, it lacks completeness for robust agent decision-making.

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 both parameters having clear descriptions. The description adds value by enumerating the types of knowledge covered (troubleshooting, DFM rules, etc.), which enriches the meaning of the 'question' and 'context' parameters beyond the schema's generic phrasing. This helps agents formulate effective queries.

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 uses a specific verb 'Search' and identifies the resource as 'injection molding simulation knowledge base'. It lists covered topics (troubleshooting, DFM rules, mesh guidelines, process parameter recommendations), clearly distinguishing this general knowledge search tool from siblings that target specific tasks like comparing materials or generating checklists.

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 states 'Use for general questions about molding, simulation setup, or problem-solving', which gives clear context for when to use this tool. However, it does not provide exclusions or alternative tools for cases that are better handled by siblings, so it lacks explicit when-not-to-use 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?

Without annotations, the description must convey behavioral traits. It discloses that the tool performs validation, calculations, and provides suggestions. However, it does not clarify read-only nature, side effects, required permissions, or response format. For a validation tool, the disclosed behavior 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 two sentences: the first states the main purpose, the second lists specific actions. Every word contributes meaning, with no redundancy or fluff. It is succinct and front-loaded.

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 has 7 parameters, no output schema, and no annotations. The description explains validation logic but leaves ambiguity (e.g., 'calculates cooling time' while cooling_time_s is an input; does it override or compute recommended?). It does not specify return format, which would be helpful given no output schema.

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 already describes all 7 parameters with 100% coverage. The description adds meaning by indicating which parameters are used for specific checks (e.g., melt_temp_C, mold_temp_C for temperature checks; injection_speed_mm_s for shear rate estimation; wall_thickness_mm and cooling_time_s for cooling time calculation), going beyond the schema's individual parameter 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 specifies the tool's primary function: validating injection molding process parameters against a material processing window. It lists specific checks (melt/mold temperature, shear rate estimation, cooling time calculation, flagging out-of-range values with suggestions), clearly distinguishing it from sibling tools that focus on comparison, checklist generation, simulation specs, property retrieval, or knowledge queries.

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 implicitly suggests use when validating process parameters, but it does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or prerequisites. The context of sibling tools helps, but direct guidance is lacking.

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