Laptop Part Finder

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

With readOnlyHint/openWorldHint already covering safety, the description adds useful behavioral context: guides are paraphrased from official manuals, ambiguous model names return candidates, uncovered topics are listed, and guides are not bench-tested. These details materially set expectations beyond annotations, though nothing is said about result volume or pagination.

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?

Three sentences, front-loaded with the core action, then filtering behavior, then caveats. The parenthetical component list is long but useful; the prose is efficient overall with little wasted wording.

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 read-only search tool with no output schema, the description conveys what comes back (step-by-step guides and a list of uncovered topics), notes ambiguity behavior, and adds the important caveat that guides are paraphrased and not bench-tested. It could say more about result format, but the essential expectations are present.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already documents all three parameters. The description adds the behavioral note that ambiguous model names return candidates, but it omits the 'query' free-text filter entirely and otherwise only restates the model/component filters, so it does not add much parameter-level value.

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 clear verb+resource ('Find step-by-step replacement guides') and scope (paraphrased from service manuals, filterable by model/component, lists uncovered topics). It does not explicitly distinguish itself from the sibling get_repair_guide, which likely retrieves a single guide, so an agent must infer the find-vs-get split.

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?

Usage is implied by 'Filter by model and/or component' and the finder framing, but there is no explicit when-to-use guidance, no exclusions, and no named alternatives such as get_repair_guide or search_parts. The agent can infer this is the discovery tool but is not told when to switch to a sibling.

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 only declare readOnlyHint and openWorldHint=false, but the description discloses the non-obvious ambiguity response shape (status "ambiguous" with candidates rather than guessing), the conditional panel_options block with its fields, and that buy_links are paid affiliate links. These are exactly the behaviors an agent could not infer from structured data.

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 with purpose, then ambiguity handling, then panel_options, then buy_links — a sensible priority order with no filler. A few sentences are long and clause-heavy, but each carries distinct operational 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?

With no output schema, the description carries the burden of describing return shapes, and it does: part fields (constraints, source URL), the ambiguous status response, panel_options structure, and buy_links. An agent has everything needed to call it and to interpret and relay results 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%, so the baseline is 3, but the description adds real meaning: the model argument accepts a slug obtained from list_models or a human-readable name, and ambiguous names are handled specially. The client parameter's affiliate-attribution purpose is covered only in the schema, which is acceptable.

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 — return every documented OEM replacement part for one laptop model — with the identifying input (model slug or name). It also delineates scope against siblings like search_parts and get_part_fits by emphasizing 'one model' and 'every documented' part with fit constraints and 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?

Gives concrete operating guidance: resolve ambiguity by asking the user and re-calling with the slug, and surface buy_links only when the user wants to buy. The panel_options instruction tells the agent what to say to the user. It stops short of explicitly naming when to prefer sibling tools like search_parts or get_part_fits.

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?

Beyond the readOnlyHint/openWorldHint annotations, it discloses that each fit carries an official source URL, that panel_options is null or an object only for Screen parts with multiple options, and, most importantly, that buy_links are paid affiliate links. Permission limits or result-size behavior are not covered, but the affiliate disclosure is the kind of context annotations cannot carry.

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 lookup and its return shape are front-loaded, and the affiliate and panel_options notes each earn their place. The panel_options clause is slightly more detail than an agent needs before calling, but it is not padding.

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?

There is no output schema, so the description had to describe the return: model list, source URL per fit, panel_options, and buy_links. That covers what an agent needs to interpret results, with only the missing part_number format and no-match behavior left unstated.

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 50%: client is documented in the schema while part_number is bare. The description compensates partially by qualifying part_number as an OEM part number, but it adds nothing about the client/affiliate-attribution parameter or the accepted part-number format.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb and resource: look up an OEM part number and list every laptop model it is documented to fit. That direction (part → models) is distinguishable from the inverse sibling get_model_parts, though the description never names it explicitly.

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?

Usage is only implied by the phrasing of the lookup. There is no statement of when to prefer this over search_parts or get_model_parts, but it does give one concrete behavioral cue: show buy_links when the user wants to buy.

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 cover the read-only, closed-world safety profile, and the description adds genuine value on top: it lists the payload contents and flags a reliability caveat ('Not bench-tested; follow the scope restrictions and the OEM manual'). It does not describe failure behavior for a missing or invalid slug.

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, front-loaded with the verb and resource, then the return contents, then the caveat. No filler and no repetition 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?

With no output schema, the description does the work of explaining the return payload and its trust level, which is the key unknown for this tool. The remaining gap is error/edge-case behavior for a slug that does not resolve.

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% and the schema provides a concrete slug example, so the baseline is already satisfied. The description adds the one thing the schema cannot: where the slug value comes from (find_repair_guides), which meaningfully improves correct invocation.

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 ('Get one repair guide by slug') and immediately enumerates what comes back: Markdown steps, page citations, official manual URL, HP service classification and scope restrictions. The origin of the slug is attributed to find_repair_guides, which cleanly separates it from the sibling search 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?

The parenthetical '(from find_repair_guides)' routes the agent: use find_repair_guides to discover slugs, this tool to fetch the full content. It also instructs the reader to honor scope restrictions and the OEM manual. It stops short of stating explicit exclusions (e.g., what to do for bench-tested data or invalid slugs), so not a full 5.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety and scope are covered. The phrase 'models covered' lightly reinforces a bounded internal catalog, but the description adds nothing about result size, pagination, or ordering for a list operation.

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 front-loaded sentence with the resource first and the optional filters second; no wasted words.

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 catalog listing with two optional filters and no output schema, the description covers scope and both parameters adequately. It could note whether all models are returned at once, but nothing essential for calling it correctly 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?

Schema coverage is 0% and neither parameter has a description, so the description carries the burden — and it does: it names both filters and supplies concrete examples (Lenovo, Dell, HP) plus the category values matching the enum. Only minor gaps (matching behavior, multiple brands) remain.

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 ('List') and resource ('laptop and Chromebook models covered'), which is distinct from the parts- and guide-oriented siblings. It doesn't explicitly name what separates it from siblings like search_parts, but the resource noun makes the distinction obvious.

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?

'Optionally filtered by' implies this is a browse/discovery entry point and that filters are optional, but there is no explicit when-to-use guidance, no mention of when to skip filters, and no reference to alternatives such as search_parts or get_model_parts.

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 declare readOnlyHint=true and openWorldHint=false, and the description is consistent with a closed catalog read. It adds genuine non-obvious context the annotations cannot express: results carry an 'official source URL' and paid affiliate buy_links (eBay, Amazon, Newegg/LPScreen), which matters for how output is presented to users.

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?

Three tight sentences, front-loaded with the search capability and followed by return shape and presentation guidance. No filler; each sentence carries new 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?

No output schema exists, and the description compensates by describing the return payload (part numbers, official source URL, buy_links). Missing only minor detail such as how limit affects results and result ordering.

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 67%: query and type are already documented in the schema, and limit is undocumented in both places. The description explains what free text should contain but does not clarify the distinct roles of the query string versus the optional type filter, which is the main ambiguity here.

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?

Uses a specific verb+resource ('Search OEM replacement parts and laptop models') and enumerates the free-text forms accepted (model name, part number, part type). It does not differentiate itself from close siblings such as get_model_parts or get_part_fits, so an agent must infer the boundary from names alone.

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?

Guidance is given about displaying buy_links when the user wants to buy, but there is no explicit statement of when to call this versus get_model_parts (known model) or get_part_fits (known part). Usage is only implied by the query examples.

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