Entogo power-equipment knowledge & tools

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

Annotations already declare readOnlyHint=true, and the description does not contradict this. It adds useful behavioral context beyond the annotation: it runs the same engine as an external calculator, produces a verdict only when an existing service is provided, and handles EMS-managed load only when an EMS is declared.

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 well-packed sentence that puts the core purpose first and then enumerates outputs in a scannable list. It is dense but not bloated; every clause adds information, though splitting it into short sentences or bullets would improve readability 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?

Given that there is no output schema, the description usefully enumerates the main computed results and the conditional verdict behavior. It also communicates the planning-only scope. It does not spell out required inputs, but the schema already covers those, so the description is sufficient for a competent agent to invoke the tool 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 only 43%, so the description must compensate, and it partially does by explaining the role of code editions, EMS, chargers, and existingService. However, it does not clarify less obvious parameters such as siteType, servicePhase, serviceVoltage, or charger sub-fields like dcfcBasis and ratingKw beyond what the schema already states.

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 as an EV-charging service capacity estimator powered by a specific engine, and lists its concrete outputs: code load, EMS-managed load, planning-basis load, transformer kVA, service rating, and a GREEN/YELLOW/RED verdict. It is clearly distinct from sibling tools like get_product or search_knowledge.

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 context for when to use the tool: for planning estimates of EV-charging service capacity under specific code editions. The phrase 'Planning estimate only' acts as an exclusion against using it for final engineering designs, though it does not explicitly name alternative tools.

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

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

Annotations already declare readOnlyHint=true, so the tool is known to be a safe read. The description adds meaningful content context ('verified company facts' plus categories), but does not disclose behavioral details such as whether the data is static, whether all facts are returned in one call, or how the response is structured. This is adequate but not rich.

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 entire description is a single, dense sentence that front-loads the key phrase 'Verified company facts' and then delivers a comma-separated enumeration of all content categories. Every item earns its place; no filler words or redundant explanations appear.

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 no-parameter, no-output-schema tool, the description gives a thorough inventory of what the agent can expect to find, enabling correct selection for questions about company facts. The only notable gap is lack of clarification on the boundary with get_lead_time, but overall the tool is well-specified for its 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?

The tool has zero parameters and schema description coverage is 100%, so the description has no parameter burden. The description helpfully lists the output content areas, which substitutes for absent output schema detail. Baseline for zero-parameter tools is 4, and no deduction 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 uses a clear verb and resource ('Verified company facts') and enumerates a specific scope: legal name, founding, headquarters, contacts, office hours, factory/capacity narrative, lead-time/warranty commitments, certifications, standards, quote channel, and URLs. This makes its purpose distinct from the sibling tools, which target products, lead times, and quote handoff.

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 strongly implies use when a user needs company-level facts, by listing the exact categories it covers. However, it does not explicitly state when to use this tool vs. alternatives such as get_lead_time, which may overlap with the 'lead-time commitments' item. No direct routing guidance is 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?

The annotations already mark the tool as read-only, and the description adds meaningful behavioral context: it reveals exact lead-time bands, the purchase-order-to-shipment basis, certification-related extensions, and a guaranteed 36-week worst case. This goes well beyond what the schema or annotations provide, though it does not describe response formatting or error behavior.

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 with the core purpose, then adds useful concrete band values in the second sentence. It contains some dense detail but every part contributes to the agent's understanding of the output.

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 only one required parameter and no output schema, the description does a good job of explaining what the agent should expect: lead-time bands, company-wide commitments, and worst-case extension behavior. It is slightly incomplete regarding the exact return structure or unknown-id handling, but these are minor for such a focused lookup 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% and the single id parameter is already described as a product id / URL slug. The description reinforces that the id refers to a product and that the result is lead-time related, but it does not add substantial new meaning beyond the 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 states the tool returns a delivery lead-time band for a product, expressed in weeks, plus company-wide commitment information. It is specific about the resource and output, though it does not explicitly distinguish itself from sibling tools such as get_product or get_company_facts.

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 the tool should be used when a delivery lead-time estimate or warranty commitment is needed, and the product-focused scope is clear. However, it does not explicitly state when to use this tool instead of a sibling like get_product or get_company_facts, nor does it mention exclusions or 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?

Annotations already declare this as read-only, and the description adds meaningful behavioral context by enumerating the exact fields returned in the full record. This is valuable because there is no output schema to communicate return structure.

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 well-structured sentence that leads with the core action and scope, then enumerates the return contents in a compact list. No filler or redundant restatement of the title.

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 single-parameter read-only lookup with no output schema, the description fully substitutes for return-value documentation by listing the record's major sections. The tool is simple enough that the description leaves no critical gap for an agent to call 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 coverage is 100%, with the id parameter described as 'Product id / URL slug'. The description's 'by catalogue id (slug)' matches and reinforces, but does not add new parameter details beyond the 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 states the tool returns a full record for one product using its catalogue id/slug, with an explicit list of returned content. This distinguishes it sharply from siblings like list_products or search_knowledge.

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 use case is clear: retrieve a complete product record when you have a specific product id/slug. It does not explicitly name alternative tools or when-not-to-use conditions, but the intended context is evident from the description.

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?

Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds that results are compact rows and that category/keyword filtering is supported, but it does not disclose pagination, ordering, result limits, or empty-result behavior. This is acceptable for a simple read-only list tool but not particularly rich.

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 one dense, efficient sentence. It front-loads the tool's purpose, then states filter options, return style, and the alternative tool with no filler. The category mappings are necessary and not wasteful.

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 optional-filter list operation, the description covers the core behavior and return granularity, and routes to get_product for richer details. Since no output schema exists, the description could mention exact returned fields, but the compact-row note is sufficient for tool selection and 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?

The input schema describes keyword but leaves the category enum values semantically empty. The description compensates by mapping power, distribution, storage, and emobility to concrete product domains, significantly improving param understanding. Keyword's case-insensitive matching is already in the schema, so the main added value is the category clarification.

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: 'List products in the catalogue', then clarifies it returns compact rows rather than full specifications. It explicitly contrasts with get_product, so an agent can distinguish this tool from its sibling without inspecting schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description states when to use get_product instead ('call get_product for full specifications'), giving a clear routing signal for the most relevant sibling. It does not explicitly discuss other siblings like search_knowledge, but enough context is provided for the main decision.

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?

Annotations already mark readOnlyHint=true, and the description adds meaningful no-side-effect context: 'stores nothing and sends nothing' and 'the user completes the request on entogo.ca or by e-mail.' This goes beyond the annotation by clarifying that the tool only returns guidance and does not submit anything on the user's behalf. No contradiction with 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?

Two dense sentences with no filler: the first enumerates the return content, the second clarifies the tool's side-effect-free handoff behavior. The most important scoping information ('request for quotation') is 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?

For a simple, read-only handoff tool with two optional string params and no output schema, the description is complete: it explains what is returned, how the product parameter is used, the external destinations, and that no data is stored or sent. An agent has enough to invoke it correctly and set user expectations.

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% for both optional string parameters. The description adds semantic value by explaining that the product parameter populates the deep link ('pre-filled with the product name'), which is not specified in the schema. The summary parameter's role as pasted content is already covered in the schema, so no extra credit is needed there.

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 ('Returns'), a specific resource ('the right channel for a request for quotation'), and enumerates exact outputs such as the transformer configurator deep link and contact details. This clearly distinguishes it from sibling tools like get_product or search_knowledge, which are about retrieving product/catalog information rather than handing off an RFQ.

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 'request for quotation (RFQ)' plus the title 'Hand off to Entogo for a quote' explicitly indicate the trigger condition: when a user wants a quote. It does not explicitly name alternative tools or exclusion cases, but the RFQ framing provides clear context for when to select 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?

Annotations already declare readOnlyHint=true and openWorldHint=false, so the description does not need to cover mutation or open-world assumptions. It adds useful context about the underlying index and output format, but it does not disclose potential behavioral nuances such as relevance ranking caveats or empty-result behavior. There is no contradiction with 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 a single dense sentence that front-loads the action and scope, uses a parenthetical to add valuable context about the Ensight index, and ends with the return type. Every phrase earns its place with no redundancy or filler.

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 two-parameter search tool with fully documented parameters and no output schema, the description sufficiently covers what it searches, where it searches, and what it returns. It stops short of describing ordering or relevance semantics in detail, but 'best-matching' implies relevance-based ranking, and the schema covers the remaining invocation details.

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 baseline applies: the schema already documents the query as a natural-language question or keywords and topK as the number of passages to return. The description adds no parameter-level detail beyond this, though it is consistent with the 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 names a specific verb ('semantic search') and a precise scope: Entogo's product, solution, manufacturing, certification, lead-time, and warranty knowledge. It also distinguishes itself from structured lookup siblings like get_product or get_lead_time by framing the tool as passage-level, natural-language search, and it states the return type (best-matching passages with source URLs).

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 when to use it—for open-ended semantic questions across the named knowledge domains—and the schema reinforces this with a natural-language query field. However, it does not explicitly state when not to use it or point to alternatives like get_product for exact product lookups or get_lead_time for specific lead-time figures, so the decision rule is left to inference.

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