制造业管理工具箱 · Manufacturing Management Toolkit

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

With no annotations, the description carries the full behavioral burden and discloses key traits: a monetization gate with a two-step payment retry pattern, an authorization-code bypass, and a minimum of 2 data points. It stops short of describing error handling for malformed points or any rate/state behavior, so it is strong but not complete.

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 core purpose is front-loaded in one sentence, followed by a structured Args block that maps cleanly to the four parameters. It is appropriately sized for a computational tool with a payment gate, though the payment instructions are slightly verbose.

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?

An output schema exists, so return values need not be explained, and the description covers inputs, the minimum-points constraint, and the payment/authorization flow. It is nearly complete for this complexity, missing only failure/error behavior and confirmation of units or output determinism.

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 0%, so the description must compensate and largely does: it defines the points JSON string format with a concrete example, explains profit, and details the payment_proof/license_key alternatives and their relationship. Minor gap: profit is described as a number yet the schema types it (and the example) as a string, and units are not stated.

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 specific verb and resource: it computes J-cost and J-return rate from cumulative production/cost points and returns improvement directions plus sensitivity analysis. This is clearly distinct from siblings like device_management, quality_management, or manufacturing_analytics, so an agent can identify the tool without opening the schema.

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 gives a working flow for the payment gate (first call empty returns payment info, then retry with proof, or use a license_key), which is genuine when-to-use guidance. However, it says nothing about when to prefer this tool over manufacturing_analytics or other cost-related siblings, so usage relative to alternatives is only implied.

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 behavioral burden and does disclose the critical, non-obvious trait: the first call returns payment information rather than results, and the tool is gated behind Alipay payment or a license key. That is exactly the kind of context an agent needs. It stops short of 5 because it says nothing about rate limits, failure modes, or what the payment-gated response looks like structurally.

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 purpose sentence is front-loaded and the args are cleanly structured per parameter with no repetition. It is appropriately sized for a three-parameter tool with a payment flow, though the example is slightly verbose.

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?

An output schema exists, so return values need not be explained, and the definition covers the essential preconditions (required device format, payment/license gating). Nothing critical for a correct call is missing, though failure-handling and post-payment response expectations are not addressed.

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, and it does: it defines devices as a JSON string with at least one entry requiring id and name, supplies a concrete example, and explains the payment_proof/license_key exclusivity. Only minor gaps remain (e.g. the full set of allowed device fields beyond id/name/model).

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 specific verb and resource: given a device list, it generates maintenance-method selection, a seven-dimension plan, inspection standards, MTBF/OEE indicators and a terminology dictionary. That is far more informative than the vague tool name 'device_management'. It does not compare itself to the siblings (quality_management, manufacturing_analytics, etc.), so it lands at 4 rather than 5.

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 gives clear invocation guidance for the payment gate: call first with payment_proof empty to receive payment info, then retry with the proof after paying, or supply a license_key to bypass payment entirely. It does not explain when to choose this tool over the sibling analysis tools, so it stops short of 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?

With no annotations, the description carries the full behavioral burden. It discloses what the call returns (services, prices, required inputs), which is useful, but never explicitly states that this is a read-only, side-effect-free operation, nor mentions permissions, caching, or rate limits. For a zero-parameter catalogue call the risk is low, hence a mid score rather than a penalty.

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 with the core purpose front-loaded and the usage hint placed in a trailing parenthesis. Nothing is padded, though the parenthetical is slightly informal and could be tightened.

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?

An output schema exists, so the description need not enumerate return fields, and with zero parameters the input side is trivially complete. The description covers what an agent needs in order to call it; only the explicit read-only guarantee is missing.

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 takes zero parameters, so there is nothing for the description to disambiguate. The baseline for a parameterless tool is 4, and the description correctly implies no input is needed.

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 gives a specific verb and resource: it lists all services in this toolbox along with prices and required inputs. That is far more informative than a tautology. It does not explicitly differentiate itself from the sibling tools, but those siblings (cost_improvement, device_management, etc.) are clearly unrelated domains, so confusion is unlikely.

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 states usage context directly: consult it before ordering or when quoting prices to a user. That gives the agent a clear trigger. It stops short of naming exclusions or explicit alternatives, so it is clear context without full when-not 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 full burden and delivers the most consequential behavior: this is a paid tool (payment_proof / license_key gate) with a documented two-step first-call-then-pay flow, plus the output format (diagnostic report + HR CSV ledger). It omits permission requirements and rate/limit behavior, but the billing/auth disclosure is substantial.

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?

Purpose and outputs are front-loaded in the first sentence, followed by per-parameter Args. The embedded JSON examples are verbose but justified given 0% schema coverage. No filler sentences, though the block is longer than an ideal description.

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?

An output schema exists, so return values need not be spelled out, and the description correctly focuses on inputs and the payment gate. The one gap is operational routing (when to use vs siblings), but for a 5-param diagnostic tool with rich param docs this is largely 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 description coverage is 0%, so the description must compensate, and it does: every one of the 5 parameters is explained, including full JSON shapes and field meanings for managers and candidates, integer typing for positions, and the mutual-exclusion rule between payment_proof and license_key. This is exactly what the bare schema lacks.

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?

States a specific verb (算/诊断) and resource (中基层管理者育成 · 梯队健康度), and enumerates the exact metrics computed (后备覆盖率, 代理试岗率, 资格达成度, 退出健康度, GL 监督时间) plus both output artifacts. An agent can tell this apart from the other HR/ops siblings without opening the schema.

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 does give a concrete invocation flow: first call with empty payment_proof returns payment info, then retry with proof, or supply a license_key instead. However, it says nothing about when to choose this tool over alternatives (e.g. tbp_problem_solving or vital_organization) or any prerequisites/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?

With no annotations, the description carries the full burden. It does disclose an important non-obvious behavior: the payment/licensing workflow (first call with empty payment_proof returns payment info, then retry with proof, or use license_key). However it omits other traits an agent needs, such as whether the operation is read-only, whether submitted data is stored, and any rate or size limits.

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 purpose is front-loaded, then parameters are enumerated in a compact Args list. Every line maps to a parameter or the core behavior; there is no filler, though the two payment parameters are explained more verbosely than the six data parameters.

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?

An output schema exists, so return-format explanation is not owed, and the payment flow is covered. Still, for an eight-parameter analysis tool with zero annotation coverage, the description leaves gaps: the JSON shape expected for each data block and the read/compute semantics of the call are unstated, which an agent would need in order to invoke it correctly.

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 and largely does: all eight parameters are glossed, and the two workflow-critical ones (payment_proof, license_key) are explained in detail, including the first-call/retry flow and the 'fill one of the two' relationship. The gap is that the six data parameters are only labeled as JSON strings without any indication of expected structure.

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?

States a specific verb and resource: multi-dimensional analysis over six named manufacturing data domains (OEE/SPC/device/inventory/delivery/cost) producing conclusions and improvement priorities. It is clear what the tool does, though it never contrasts itself with plausible siblings such as cost_improvement, device_management, or quality_management that overlap with its input domains.

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 only usage rule given is '至少提供其中一项' (supply at least one of the data blocks), which is a prerequisite rather than routing guidance. It never says when to prefer this tool over the sibling analytics tools or when it is the wrong choice.

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, and it does disclose important behavior: the first call is payment-gated and returns payment instructions, and a license_key bypasses payment. It omits details like idempotency, timing, or what happens if an invalid proof/license is supplied.

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?

Front-loaded one-line purpose followed by a compact Args block; every entry adds information rather than restating the schema. Sized appropriately for a tool at this complexity.

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?

An output schema exists, so return values need not be described, and the description adequately covers the payment gating, license alternative, and module scoping. The main residual gap is the unexplained Q1–Q12 module taxonomy, which an agent would need in order to target a specific module.

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 0% schema description coverage, the description must compensate and does: it documents all five parameters, including the payment-or-license mutual-exclusion rule and the ALL/Q1–Q12 module switch. It never explains what modules Q1–Q12 actually correspond to, leaving that opaque.

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?

States a specific verb and resource ('生成...品质文档') plus concrete scope: six stages and named deliverables (QFD, DFMEA, 控制计划). It is clearly distinguishable from the sibling tools, but it never explicitly contrasts itself with any of them, so it falls short of a 5.

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?

Gives strong in-tool workflow guidance: how to select a single module (Q1–Q12) versus ALL, and a full payment/licensing flow (leave payment_proof empty on first call, retry after payment, or supply license_key). It does not address when to prefer this tool over siblings like manufacturing_analytics or cost_improvement.

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 and does disclose important non-obvious behavior: the first call acts as a payment/preview gate, a license_key bypasses payment, and the batch is capped at 300 entries. It omits things like processing time, failure behavior on malformed entries, and whether calls are idempotent.

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 purpose sentence is front-loaded and dense with specifics, followed by a structured Args block. It is longer than typical but each part carries information; no filler sentences are present.

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 non-trivial (paid gate, batch limit, structured input, two output artifacts) and an output schema exists, so return values needn't be re-explained. Between the purpose sentence, the 300-entry limit, and the payment/license flow, an agent has everything needed to invoke it correctly.

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, and it does: sources is documented as a JSON array string of 1–300 entries with a full field-by-field example, and the interaction between payment_proof and license_key (either/or, empty-first-call semantics) is spelled out clearly. This adds substantial meaning beyond the bare schema 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 names a precise verb+resource and enumerates the concrete processing steps (six-category classification, A/B/C risk grading, inspection-cycle recommendation, hardware-priority violation detection) plus the two artifacts produced (audit report + normalized CSV for EHS/CMMS). It is fully self-explanatory about what the tool does, though it never positions itself against any sibling tool.

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 gives strong invocation guidance for the paid flow (first call with empty payment_proof returns payment info; retry with payment_proof or license_key, 'fill one of them'), but it never states when to choose this tool over sibling tools or what prerequisite inputs must exist. Usage is implied rather than contrasted with alternatives.

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, and it does disclose the key non-obvious behavior: a first call without payment_proof returns payment information, and the call must be retried with proof, or a license_key can bypass it. This is genuinely important auth/payment context. It omits other traits such as whether the analysis is deterministic or how long the report is, keeping it out of 5 territory.

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?

Purpose is front-loaded in the first sentence, followed by a clean per-argument breakdown using the standard Args convention. Every section earns its place, though the payment paragraph repeats the license/payment relationship slightly.

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?

An output schema exists, so return values need not be described, and the description covers the invocation flow, parameter semantics, and the payment gate. The main gap is the absence of trigger conditions relative to sibling tools, which is the only missing piece for calling it correctly.

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, and it does: each of the five parameters is explained with meaning plus a concrete example (e.g. the '3号注塑机' problem sample, quantified data, target threshold). It also clarifies that payment_proof and license_key are alternatives, resolving the mutual-exclusivity that the bare schema cannot express.

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 names a specific verb+resource: analyzes a management/equipment/quality problem using named methodologies (Toyota 8-step, 5Why three layers, 8×4 matrix) and emits a full analysis report. That is concrete and actionable. It stops short of explicitly distinguishing itself from siblings such as quality_management or device_management, so it is clear but not fully differentiated.

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 choose this tool over the sibling tools (quality_management, device_management, cost_improvement), which are the obvious overlaps for a TBP analysis. The payment instructions describe an invocation flow but not selection criteria, so usage must be inferred.

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 and does well: it discloses that blank trust/hope/value/challenge scores are estimated from industry benchmarks, and it explains the two-phase paid-access model including the license_key alternative. It omits any statement about rate limits, data retention, or what happens on invalid credentials, so it is strong 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 purpose sentence is front-loaded and the argument list is dense and scannable, with no filler prose. The Args block is somewhat list-like, but each line delivers distinct parameter information rather than restating 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?

Because an output schema exists, the description need not narrate return values, and it focuses correctly on inputs and the access/payment flow. All ten inputs are covered and the two-call pattern is explained, though the agent receives no hint about prerequisite data quality or expected response shape 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% (properties carry only titles and empty-string defaults), so the description must compensate and does: it explains every one of the ten parameters, including the 0–5 scale and optionality of the four dimension scores, the benchmark fallback, and the exact roles of payment_proof and license_key. This is materially more meaning than the bare schema provides.

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 names a specific verb and resource: diagnosing organizational vitality and producing a four-dimension (trust/hope/value/challenge) construction plan with an implementation checklist. This is far more informative than a tautology, and the concrete deliverables make the tool's output identifiable. It does not, however, distinguish itself from siblings like tbp_problem_solving or cost_improvement, leaving an agent to infer the boundary.

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 gives clear operational context for the payment-gated flow: a first call with payment_proof empty returns payment information, and the caller retries with proof after paying, or supplies license_key to bypass payment. This is genuine when/how guidance for actually invoking the tool. It offers no guidance, though, on when this diagnosis is preferable to the sibling problem-solving or quality tools.

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