SvGrid

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 does communicate the core behavior: returning full text for a given slug or demo id. But it does not disclose response format, error behavior, or any non-obvious limitations, leaving meaningful gaps for an agent invoking the 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 sentence with the purpose front-loaded and no filler. The examples are tightly packed and directly support correct invocation.

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, no-output-schema tool, the description covers the necessary invocation details: what the tool does and exactly what forms the id can take. It does not explain the relationship to the search tool or address potential edge cases, but nothing critical appears missing for a tool this simple.

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 is 3. The description adds concrete examples ('help/columns/column-definitions' and 'demo:11-stock-market') and reinforces the two accepted formats, but this is a modest addition beyond what the parameter description 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 names a specific verb ('Return') and resource ('full text behind a search result'), and gives concrete examples of the two accepted input forms. It does not explicitly contrast itself with sibling tools such as get_example_source or check_svgrid_code, so it stops 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 'behind a search result' implies that this tool is for retrieving full content for items surfaced by search, and the id formats are clearly explained. However, it never explicitly states when to prefer this tool over alternatives or when not to use it, so usage guidance is only implied rather than stated.

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?

There are no annotations, so the description carries the full burden. It does disclose that the API is public and version-scoped, and that content is grouped by category, which is useful. However, it does not explicitly state that the operation is read-only, describe the response format, or mention any output 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 a single, information-dense sentence. Every element earns its place: the resource, the grouping principle, the included categories, and the version scoping. 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?

For a no-parameter, read-only reference lookup tool, this description is largely complete. It tells the agent what data is available and that it is version-specific. A small gap is the lack of any statement about the response shape or whether it returns a list, tree, or paginated document, but this is minor for such a simple 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?

The tool has zero parameters, so there is no parameter semantics to explain. The baseline of 4 applies because no schema detail is missing and the description doesn't need to compensate for undocumented 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 clearly identifies the tool's resource as the SvGrid public API reference and adds meaningful scope by listing categories and noting version tracking. It distinguishes itself from sibling tools like search or list_examples, though it lacks an explicit verb such as 'retrieve' or 'return'.

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 explicit guidance is given about when to use this tool instead of alternatives like search, fetch, or get_example_source. The wording implies it is for browsing the API reference by category, but the conditions for choosing it over search are left unstated.

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 behavioral burden. It tells the agent the tool returns the complete source file rather than a summary, and frames it as copyable working code; 'Return' signals a read operation. It doesn't mention error cases, but for a simple getter this is adequate.

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 sentence front-loads the action and object, gives a concrete example, and appends a clarifying contrast ('not a summary'). Every phrase earns its place with no redundant 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 single-argument read tool with no output schema, the description tells the agent what it will receive (full .svelte source) and how to target a demo. It could additionally mention what happens for unknown ids, but overall the definition is complete enough 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?

The schema already describes the single 'id' parameter at 100% coverage, including the same example. The description adds no new parameter-level detail beyond repeating the id/keyed-by-id concept, so baseline 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 ('Return') and identifies the exact resource: the full '.svelte' source for one demo, keyed by id. It even gives a concrete example and clarifies that it is not a summary, which distinguishes it from generic fetch/list/search siblings.

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 clearly states the context for calling this tool: you need the full source code of a specific demo by id. It doesn't name alternatives like list_examples or search, but 'one demo by id' and 'not a summary' provide enough scope guidance for selection.

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 behavioral burden. It explains the no-arguments behavior, the filtering behavior, and the returned fields. It does not mention ordering, case sensitivity, or pagination beyond the schema's limit default, but the high-level behavior is well disclosed for a simple browsing 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?

Two sentences with no filler. The resource and fields are front-loaded, followed by a compact workflow. Every clause 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?

The description covers the full lifecycle from index browsing to filtering to reading a detail page, names the returned fields, and points to the relevant sibling tool. For a simple read-oriented tool with no output schema, this is complete enough 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 description coverage is 100%, so the parameters are already well documented. The description reinforces that query and category are filters, but adds little semantic detail beyond the schema. Baseline 3 is appropriate.

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 ('Browse') with a clear resource ('SvGrid demo catalogue') and lists the exact fields returned. It also names the follow-up sibling tool (get_example_source), distinguishing the listing/browsing function from reading a single example.

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 explicit usage guidance: call with no arguments for the category index, then filter by query or category, then read one example with get_example_source. It does not explicitly contrast with the 'search' sibling, but the intended workflow is clear enough for correct selection.

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 behavioral disclosure burden. It states the search is ranked, returns results ordered by relevance, includes an excerpt and an id, and positions the tool as a docs lookup. It stops short of describing edge cases like empty results or formatting, but for a search tool this is solid context.

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 with no filler. The first sentence states what the tool does and what it returns; the second gives direct usage guidance. Everything 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 two-parameter search tool with full schema coverage, the description is nearly complete. It even explains how the output connects to the fetch sibling. A more explicit output shape or mention of limit behavior would help, but nothing essential is missing 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 coverage is 100%: both query and limit are described with types, defaults, and an example. The description adds no parameter-level meaning beyond that, so the schema already does the heavy lifting and the baseline 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 names a specific verb and resource: 'Ranked full-text search across the SvGrid documentation and demo catalogue.' It also clarifies what the tool returns (best pages, excerpt, id for fetch), which distinguishes it from siblings like fetch and get_api_reference.

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 timing guidance: 'Use this before writing SvGrid code so the API names come from the docs rather than from memory.' It does not explicitly contrast with alternatives like get_api_reference or list_examples, but the use-before-coding context is strong.

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 disclosure burden and does a solid job: it states what is validated, that it compiles the code, that it returns line-numbered diagnostics with an exact replacement per issue, and that `fixed` is only included when the correction is exact. It does not say whether the file is modified on disk or what happens on unparseable input, so it falls short of 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 imperative to verify before handing off is front-loaded, followed by what is checked and what is returned. It is dense but every clause carries information. The middle sentence is long and list-heavy, slightly taxing to parse, which keeps it off 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?

No output schema exists, so the description must explain returns, and it does: line-numbered diagnostics with exact replacements plus an optional `fixed` copy. Combined with the explicit usage loop, an agent has what it needs to call this correctly. Missing only edge-case behavior on invalid or oversized input.

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 both `source` and `filename` are already documented in the schema, making 3 the baseline. The description adds no extra detail about `filename` (the schema already says it selects which rules apply) and nothing about size limits or expected `source` scope beyond 'full file contents.'

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?

A specific verb ('Verify') plus a specific resource ('SvGrid code') and, crucially, an enumeration of what is checked: prop names, ColumnDef keys, grid API methods, importable symbols, theme files, and Svelte 5 runes rules. An agent can tell this apart from siblings like svgrid_preview or get_api_reference without opening either 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?

Explicit timing guidance: run it BEFORE handing code to the user, on every file written that uses SvGrid, and re-run after applying `fixed`. This tells the agent exactly when to invoke it. It does not name alternatives or say when not to use it (e.g. versus svgrid_preview), which keeps it short of a 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 burden and does well: it discloses the non-UI fallback (returns a text summary, 'always safe to call'), the interactivity traits (sortable, filterable, scrollable), and that demo previews return source as text. It doesn't discuss performance or rendering limits beyond a soft row-count hint.

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 tight sentences, front-loaded with the core action and ending with the safety reassurance. Every clause earns its place; nothing is repeated from 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?

For an 8-parameter tool with no annotations and no output schema, the description covers the essential gaps: the fallback return behavior, the two input modes, and a size caution. Output format specifics are left implicit, but the fallback sentence largely covers the main uncertainty.

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 schema already documents every parameter including defaults for sortable/filterable. The description only restates the two input modes, adding no syntax or format detail beyond the schema — baseline 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?

States a specific verb and resource — 'Render a REAL, interactive SvGrid' — with the two input modes (columns/rows or demo id) spelled out. An agent can distinguish it from check_svgrid_code (validation) without opening either 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?

Gives clear usage contexts: 'before they build it' and 'after svgrid_check_code to demonstrate the result', and points to svgrid_search for demo ids. It lacks an explicit when-not condition, but the context is unambiguous.

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