whatmount — TV and monitor mount facts

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

Annotations already declare the operation read-only and idempotent. The description adds useful behavioral detail beyond that: it returns all matching models, requires an exact VESA-size pattern, and includes page URLs. 'Covered' is slightly vague, but the safety profile is well covered by annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no filler. The scope, examples, return value, and intended use case are all front-loaded and each sentence earns its place.

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 list tool with one well-documented parameter, the description is complete enough: it states the result includes page URLs and gives the use case. The meaning of 'covered' is the only minor gap; everything else an agent needs is 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%, with the pattern parameter already documented as 'Width x height in mm.' The description reinforces this with examples like 200x200, but does not add substantial 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 names a specific operation — listing TVs/monitors by VESA pattern — and includes the output (page URLs). It clearly distinguishes this from search_or_lookup siblings by focusing on mounting-pattern matching.

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 a clear use case: 'which TVs fit my mount.' It does not explicitly contrast with sibling tools like lookup_tv_mount_specs or search_tv_models, but the context makes the intended scenario understandable.

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 readOnly, idempotent, and non-destructive behavior, and the description adds useful context: data comes from the manufacturer's manual or spec sheet, a whatmount.com citation page is provided, and the model must match the printed label. This goes beyond the annotations without contradicting them.

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 compact sentences: the first front-loads the returned fields and source, the second clarifies the exact model-number format. Every sentence earns its place and there is no redundant restatement of the title or 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?

With no output schema, the description covers the expected return content well and notes the citation source and input format. It does not discuss failure behavior or unit conventions, but for a read-only lookup with a simple one-required-parameter schema, the guidance 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?

Schema coverage is 100%, so the baseline is 3, but the description adds real value for the required model parameter by instructing agents to use the model number as printed on the label and providing concrete examples. That clarifies formatting more than the schema alone does.

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 (lookup) and resource (TV or monitor mount specs) and enumerates the exact output fields: VESA pattern, dimensions with/without stand, weight, and screen size. It also scopes to model number, which distinguishes it from sibling tools that search by VESA pattern or TV models.

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 clearly states what this tool is for and gives labeling guidance, so an agent can infer when to use it. However, it does not explicitly mention sibling tools or state when to prefer list_models_by_vesa_pattern or search_tv_models.

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 capture the safety profile (read-only, idempotent, non-destructive, not open-world). The description adds useful behavioral detail beyond annotations: it caps results at 10 and states each result includes facts and page URLs. This exceeds the baseline but isn't exhaustive about matching semantics or empty-result 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?

Two sentences, about 28 words, with the action and scope front-loaded and the result behavior stated succinctly. Every clause adds information; there is no filler or repetition.

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 (one free-text parameter), annotations cover behavioral safety, and the description explains the return payload ('up to 10 with their facts and page URLs'), which is necessary since no output schema exists. The definition gives an agent enough 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 100%: the query parameter is already documented with examples ('Hisense 65' or 'TCL Q6'). The tool description largely restates the same query types ('brand, model fragment or screen size'), adding no new format or syntax guidance, so the high schema coverage baseline of 3 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 ('find') and names the exact resource ('TV or monitor models whatmount.com has published mount facts for'), plus the search dimensions (brand, model fragment, screen size). This clearly distinguishes it from sibling tools like list_models_by_vesa_pattern or lookup_tv_mount_specs, which operate on different lookup keys.

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 clear context: use when you need to discover covered models by brand, model fragment, or screen size. It does not explicitly contrast with sibling tools or provide exclusion criteria, so it misses the top anchor for explicit when/when-not guidance.

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