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

With no annotations provided, the description carries the full burden of behavioral disclosure. It fails to explain side effects (e.g., whether confirmation is recorded, if revisions replace existing data), return format, or any state changes. The phrase 'evaluate lessons' hints at processing but lacks detail on outcomes or requirements.

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, which is concise in length, but it lacks clarity and structure. It front-loads multiple ambiguous actions instead of stating a primary purpose. While not verbose, the conciseness is not effective because it sacrifices comprehensibility.

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 4 parameters (2 required) and an output schema, the description is grossly inadequate. It doesn't specify required inputs, optional behavior, or what the tool returns. An agent would need to open the schema and infer semantics from parameter names alone, which is risky. The description adds minimal context beyond the 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?

Schema description coverage is 0%, so the description must explain all parameters. It indirectly references confirmation_text and review_revision_text ('confirm' and 'revise its pending review'), but it does not clarify design_id (which is required) or lesson_candidates_json (which is a JSON string with default '[]'). The description fails to define the role of each parameter or their expected formats.

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 'Confirm the model, evaluate lessons, or revise its pending review' is vague and ambiguous. It lists three verbs but doesn't clearly define the resource ('the model' is unclear) or how these actions relate. It doesn't differentiate from sibling tools like design_lesson_decide or design_status, leaving an agent uncertain about the tool's exact function.

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 guidance on when to use this tool versus alternatives. It doesn't mention any conditions, prerequisites, or exclusions. An agent cannot determine whether to call design_confirm or design_lesson_decide from the description alone.

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 present, the description carries the behavioral disclosure burden. It honestly discloses that results are preliminary and not a strength certification, and directs the agent to the result's limitations for what is not evaluated. This is useful transparency beyond the raw schema.

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 compact and front-loaded: the core purpose is stated in the first sentence, and the limitation/qualification follows immediately. There is no filler, and every sentence contributes actionable information.

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 the tool's purpose and key limitation, and an output schema exists to explain return values. However, with 9 parameters, no annotations, and no input guidance beyond names, the description leaves the agent to infer several input requirements, making it only minimally complete for a tool of this complexity.

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 0%, and the description provides no parameter-specific meaning. Although some parameter names are self-explanatory (power_kw, pinion_speed_rpm), the description does not clarify expected units, value ranges, material formats, or the purpose of options_json. The description must compensate for low schema coverage but does not.

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 opens with a specific verb and resource: 'Size a spur gear drive' and enumerates the covered aspects (ratio, teeth, module, forces, stresses, shaft, bearings). This clearly distinguishes the tool from the sibling workflow tools, none of which perform gear sizing.

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 usage context by stating this is 'Preliminary sizing evidence, not a strength certification,' which tells the agent when the tool is appropriate and when it is not. It does not name explicit alternative tools, but the exclusion is meaningful and actionable.

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 of behavioral disclosure. It only hints at a possible non-blocking intent with 'without making retrieval a CAD gate,' but it does not disclose side effects, retrieval scope, response behavior, or operational constraints.

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 short and front-loaded, with no redundant filler. However, it is more under-specified than usefully concise, and the CAD-gate phrase adds ambiguity rather than structure.

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 annotations, 0% parameter coverage, and 5 parameters including two required fields, this one-line description is severely incomplete. It does not explain how an agent should construct a query, what the required inputs mean, or what response behavior to expect.

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 0% with 5 parameters, and the description mentions none of them. The agent is left without any explanation of design_id, query, required, features_json, or used_knowledge_ids_json.

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 states a clear verb and resource: 'Retrieve applicable knowledge.' It is specific enough to convey the basic action, though it does not explicitly distinguish this tool from its siblings, and the phrase 'without making retrieval a CAD gate' is somewhat jargon-heavy.

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 given about when to use this tool instead of siblings such as design_status, design_record_result, or design_lesson_decide. There are no conditions, exclusions, or alternative tool references.

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 must disclose behavioral traits but only reveals that the tool changes publication state. It does not mention authorization requirements, reversibility, effects on non-selected lessons, or how the default decision applies. The 'decline publication' hint is a thin signal with no detail.

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 with a clear verb and scope. It is concise and readable, but the brevity is achieved at the cost of leaving important operational details unexplained. Still, it earns a 4 for clarity of structure.

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 mutating decision tool with no annotations, the description lacks critical context: how to specify approval vs decline, how to choose which lessons to act on, and what design_id refers to. The presence of an output schema does not compensate for missing parameter semantics and workflow guidance.

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 0%, so the description must compensate for undefined parameters. It does not explain what decision_text should contain, how selected_lesson_numbers_json selects lessons, or what the default '[]' means. The phrase 'all or selected' hints at the selection parameter but leaves format and values ambiguous.

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 action — approving or declining publication of review-card lessons — and distinguishes between approving all or selected lessons. It is specific about the resource and operation, though it does not contrast with sibling tools like design_confirm or design_record_result.

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?

There is no guidance on when to use this tool versus alternatives such as design_confirm or design_record_result. The description implies it is used after reviewing lessons but does not state prerequisites, workflow position, or when to choose this tool over siblings.

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 of behavioral disclosure. It clearly communicates a read-only listing behavior, the workspace-wide scope, and the fact that each returned item includes current state. It does not discuss pagination or ordering, but that is reasonable for such a simple tool with an output schema.

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?

A single sentence that delivers all key information immediately: what is listed, at what scope, and what information is included. There is no filler or 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?

For a zero-parameter list tool with an output schema, the description is nearly complete. It clearly states the operation and the returned content. It falls slightly short only by not addressing whether all workspace design jobs include completed/failed/archived jobs or just currently active ones.

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 parametersches and 100% schema description coverage, so the baseline is 4. The description adds no parameter-specific meaning, but there are no parameters to explain.

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 ('List'), a clear resource ('every design job in the workspace'), and a qualifier ('with its current state'). It does not explicitly differentiate from sibling tools like design_status, but 'every design job' signals a broad listing operation.

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 an agent needs an overview of all design jobs, but it provides no explicit when-to-use guidance or distinction from sibling status-related tools. It leaves the choice between design_list and design_status to inference.

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 of disclosing behavior. 'Report' implies a read-only operation, but the description does not explicitly state that it is safe, whether it requires prior steps (e.g., a design must exist), or whether it might return stale data. The behavioral implications are only implicit, leaving room for misinterpretation.

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, compact sentence with no fluff. It front-loads the key action ('Report validation defects') and the scope ('this design'). It earns high marks for efficiency, though the brevity borders on under-specification, which is why it is not a 5.

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 tool with an output schema, the description is minimally adequate: it identifies the resource and the deliverable. However, it lacks context about the design lifecycle, what constitutes a 'validation defect', and how this tool relates to siblings like design_record_result or design_lesson_decide. The output schema covers return values, but the description does not fully equip an agent to call it correctly in a broader workflow.

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 schema description coverage at 0%, the description should compensate by explaining design_id. It only alludes to 'this design' without clarifying how to obtain or format the identifier, what values are valid, or any relationship to other design tools. The parameter is left as an opaque string, providing minimal additional meaning beyond the schema's type information.

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 verb 'report' and resource 'validation defects' are specific, and the phrase 'this design corrected or still carries' indicates the scope is tied to a particular design. It is clearly distinct from sibling tools like design_status or design_list, which focus on status or listing rather than defect reporting. A minor ambiguity is what 'validation defects' exactly encompasses, but the core purpose is clear.

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 no guidance on when to use this tool versus alternatives. It does not mention prerequisites, typical scenarios, or any exclusions (e.g., 'use design_status for overall health'). An agent must rely on the tool name and vague wording to decide when to call it, which is insufficient.

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 must carry the full burden of behavioral disclosure. It implies a write operation ('Bind') but does not state consequences, reversibility, permission requirements, or side effects. It also fails to disclose what happens to existing records or whether the operation is idempotent. This leaves significant uncertainty for an agent.

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, compact sentence that leads with the core action and resource. There is no redundant phrasing, and it is appropriately sized for the tool's function. Every word contributes to understanding the primary 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 having an output schema, the description omits critical operational context: what inputs are expected (parameter meanings), how they interrelate, and any workflow prerequisites. Given the tool has four required parameters and no annotations, this is insufficient. An agent cannot confidently construct a valid call without further elaboration.

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 0%, so the description must explain the parameters. It only mentions 'passed JSON, Markdown, and PNG evidence,' which hints at the evidence_paths_json parameter but does not map it explicitly. It completely omits design_id, model_path, and validation_report_path, leaving their roles unexplained. The description adds minimal value beyond the schema's bare property names.

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 the specific verb 'Bind' and clearly states the action: linking an exact FCStd hash to evidence files (JSON, Markdown, PNG). It specifies the resource and the operation, and it is distinct from sibling tools that deal with design lifecycle events rather than result recording. This leaves no ambiguity about what the tool does.

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 guidance on when to use this tool versus alternatives. It does not mention any preconditions, sequencing with other design tools, or exclusions. An agent has to infer that this is used after validation produces evidence, but no explicit context is given.

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 behavioral disclosure burden. 'Start or resume' implies a state-changing operation, but it does not disclose side effects, required permissions, what happens to an existing design on resume, whether validation occurs, or how approval_text is used. This is a significant transparency gap for a mutation-like tool.

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, compact sentence with no filler words; the main action and condition are front-loaded. It loses a point only because it is so terse that it omits information the agent needs, but judged purely on conciseness and structure, it is 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?

This is a complex tool with 7 parameters, 6 required, no annotations, and 0% schema coverage, yet the description provides almost no operational context. While an output schema exists, it does not offset the absence of parameter semantics, behavioral side effects, or workflow guidance. The description is far from sufficient for correct invocation.

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 0% and there are 7 parameters, yet the description adds no parameter-level meaning. It only alludes to 'natural-language direction approval,' which loosely maps to approval_text, but design_id, title, model_classification, requirements_json, proposal_summary, and source_path are entirely unexplained. The description fails to compensate for the absent 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 clearly states the action ('Start or resume a design') and the triggering condition ('after natural-language direction approval'). It is a specific verb+resource statement, though it does not explicitly contrast itself with sibling tools like design_confirm or design_status, so it falls just short of full sibling differentiation.

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 phrase 'after natural-language direction approval' gives an implied usage context: the tool should be invoked once approval has been obtained. However, there is no explicit guidance about when not to use it, when to use design_confirm instead, or what prerequisites must be satisfied (e.g., whether design_id must already exist for resume).

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 behavioral burden. 'Read current... state' clearly signals a non-mutating snapshot operation, which is helpful. However, it does not disclose freshness, authentication needs, error behavior, or whether the design must be active, though for a simple status read this is a moderate gap.

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 with no filler, front-loaded with the operation verb 'Read'. Every word earns its place, and it is appropriately sized for a simple status tool.

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 output schema exists and there is only one required parameter, so the baseline completeness is decent. However, the description fails to explain design_id or clarify when to choose design_status over design_system_status, leaving a clear gap for correct invocation.

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 0%, and the description never mentions design_id or how to obtain it. The required parameter is completely undocumented in both the schema and the description, so the agent gets no guidance on its meaning, format, or source.

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 ('Read') and names concrete state domains: model, validation, confirmation, and lesson state. This makes the tool's purpose clear and distinguishes it from mutation-focused siblings like design_start, design_confirm, and design_record_result. It does not explicitly distinguish itself from design_system_status, but the listed state categories provide enough specificity.

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?

There is no guidance on when to use design_status versus related tools such as design_system_status or design_list. No prerequisites, exclusions, or alternative-selection rules are provided, leaving the agent to infer the right context.

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 must fully disclose behavior. It implies a read-only inspection via 'inspect', but it does not state whether external calls are made (e.g., for provider readiness), what local data is accessed, whether any side effects occur, or what permissions are required. This is minimal behavioral disclosure.

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 of nine words, front-loading the verb and the key domains with no filler. It is maximally concise while still conveying the 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?

For a zero-parameter tool with an output schema, the description is largely sufficient, but it does not address the relationship to the similarly named 'design_status' or clarify whether the readiness check is purely local or involves external services. This leaves some ambiguity for an agent deciding between sibling tools.

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, and the description correctly avoids inventing any. Per the rubric, a schema with no parameters earns a baseline of 4, and the empty input schema already covers this fully.

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 the specific verb 'inspect' and names the resource domains ('local design, FreeCAD, knowledge, and provider readiness'), clearly conveying a status/health-check purpose. It does not explicitly distinguish itself from the sibling 'design_status', so it stops short of full differentiation.

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 like 'design_status' or 'standard_part_sources_status'. The intended invocation context is only implied by the name and domain list, with no explicit conditions, exclusions, or routing.

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 must disclose side effects, authorization needs, or data requirements. It only says 'register' (implying a write) and 'full provenance,' but doesn't say whether registration is idempotent, overwrites existing parts, or enforces validation requirements.

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?

A single sentence is economical and front-loaded, but for an 8-parameter registration tool it is under-specified rather than appropriately concise.

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?

Even with an output schema, the agent lacks guidance on required parameter semantics, registration behavior, and prerequisites. The description conveys only the high-level intent, leaving too much for the agent to infer.

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 0% and the description adds no parameter-level meaning. The phrase 'full provenance' only vaguely gestures at source URL/validation metadata without mapping to any of the eight parameters.

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 ('register') with a well-defined object ('validated downloaded standard part') and a scope qualifier ('with full provenance'). It is clearly distinct from sibling tools, which query providers or source status rather than creating a registration record.

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 statement of when this tool should be used versus alternatives, and no exclusions. The only implied context is that the part must already be validated and downloaded, but the description does not explain prerequisites or workflow placement.

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?

The description indicates this is a listing/read operationabbvie and implies no mutation, which is behaviorally useful. However, with no annotations, it does not disclose whether any authentication, trust-order configuration, or side-effect behaviors apply; it stays at a basic level.

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?

One clear sentence that immediately conveys the action, resource, and ordering constraint. There is no filler or 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?

The tool is simple and has an output schema, so the missing return-value documentation is acceptable. However, the complete silence on the optional 'category' parameter and the lack of sibling differentiation leave gaps that could lead to an incorrect invocation or tool choice.

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 0%, and the description does not mention the 'category' parameter at all. The agent cannot determine whether category filters, sorts, or otherwise affects the list of standard-part sources.

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 ('List') and clearly identifies the resource ('standard-part sources') and a distinguishing characteristic ('in configured trust order'). It does not explicitly differentiate itself from its similar sibling standard_part_sources_status, so the uniqueness is implied rather than stated.

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?

There is no guidance on when to use this tool versus standard_part_sources_status or standard_part_download_register. The phrase 'configured trust order' hints at context, but no conditions, exclusions, or alternatives 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 provided, the description carries the full burden of behavioral disclosure. 'Inspect' implies a read-only operation, but the description does not disclose potential error states, whether the binding can be unconfigured, or what kind of data the status check returns.

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 with no filler or redundant phrasing. Every word adds semantic 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?

The tool is simple, has no parameters, and an output schema is present, so the description does not need to explain return values. The only slight gap is not defining what 'standard-part catalog binding' means, but for a low-complexity status tool this 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?

The tool has no parameters, so the schema fully covers this dimension. The description does not need to explain parameter semantics, and the baseline of 4 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 uses a specific verb ('inspect') and names a concrete resource ('configured standard-part catalog binding'), making the tool's purpose clear. It reads as a status/read operation distinct from sibling tools like standard_part_providers_get or standard_part_download_register, though it does not explicitly point out the distinction.

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 about when to use this tool versus its siblings. There is no mention of alternatives, exclusions, or the larger workflow context in which the binding status matters.

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