Grandpa's AI

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

No annotations are provided, so the description carries the full behavioral burden, and it discloses almost nothing beyond the basic read action. It does not say whether the read is safe/reversible, what happens when the path is invalid or missing, or how large a returned article may be.

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 short sentences, zero filler, with the core action front-loaded and the input-provenance hint immediately after. Nothing is redundant with 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?

For a simple two-parameter read tool with a fully documented schema and no output schema to explain, the description covers the essential action and input source. The remaining gap is the absence of error/miss behavior, which no structured field supplies since there are no annotations.

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%, with both 'path' and 'format' documented including an example path and a description of each enum value, so the baseline of 3 applies. The description adds no parameter meaning beyond reinforcing that 'path' comes from search results.

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 ('Retrieve a full research article') scoped by a lookup key ('by its path'), which separates it from search_research and get_latest_research. It stops short of naming the sibling tools explicitly, so it lands at clear-but-not-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?

'Use paths from search results' gives concrete upstream context for how to obtain the required argument, effectively routing the agent through search_research first. No when-not guidance or explicit alternatives are stated, so it does not reach the top tier.

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 behavioral burden, and it does very little. It implies a read but does not state permissions, ordering semantics (what 'most recently updated' means on ties), pagination, or the shape of the result. Only the ordering intent is disclosed.

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 zero redundant or filler content. Every word 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 tool with no annotations and no output schema, the definition is too thin: it does not describe the return shape, ordering guarantees, or how results relate to the sibling search/get tools. The two well-documented parameters are the only thing making it callable at all.

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 'count' parameter documents its default of 5 and max of 20, and 'category' is described as an optional top-level filter. The description adds nothing beyond that, so 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?

States a specific verb ('get') and resource ('research articles') with a clear qualifier ('most recently updated'), so the agent knows it returns the newest items rather than arbitrary ones. It does not differentiate itself from the sibling search_research or get_article, 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?

There is no explicit when-to-use guidance and no mention of the alternatives (search_research, get_article, list_categories) that could be confused with it. Use is only implied by the phrase 'most recently updated', leaving the agent to infer that this is the 'browse newest' path versus search.

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 load. It signals a read-only fetch and discloses the return shape (hierarchical tree including page URLs), which is useful since there is no output schema. It says nothing about size, caching, or any auth considerations, so it remains only partially transparent.

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 well-formed sentence with the resource front-loaded and no filler. It is appropriately sized for a no-argument tool, though it is lean enough to feel slightly under-specified.

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 zero-parameter, no-output-schema tool the description covers the essentials of what is returned, but it omits any sense of the tree's breadth, freshness, or how it relates to the other retrieval tools. Just enough to call it, not enough to call it confidently among siblings.

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 the baseline of 4 applies. There is no parameter-level ambiguity for the agent to resolve.

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?

Clear verb+resource ('Get the full hierarchical topic tree') plus scope details (grandpasai.com, includes page URLs). It does not, however, distinguish itself from the similarly-named sibling list_categories, leaving the agent to guess which one retrieves the tree.

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 when-to-use guidance, no prerequisites, and no mention of alternatives such as list_categories or search_research. The agent must infer usage purely from the tool name.

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 of behavioral disclosure. While 'List' implies a read-only operation and the description mentions article counts (a return detail), it does not explicitly state that the tool is non-mutating, require no special permissions, or mention any rate limits or caching 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, front-loaded sentence with no wasted words. It efficiently conveys the tool's purpose and the key data it returns.

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 and no annotations, the description needs to convey what the agent gets back. It does specify categories, subcategories, and article counts, which is adequate for a simple listing tool, though it leaves some ambiguity about the exact return structure (flat list vs. hierarchy).

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 the baseline is 4. The description adds no parameter information, which is appropriate since there is nothing to document.

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 ('List') and resource ('research categories and subcategories') and adds that article counts are included. It is clear what the tool does, but it does not explicitly differentiate itself from the sibling get_topic_tree, which likely also returns category/topic structures.

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 use this tool versus alternatives like get_topic_tree or search_research. The description merely states what the tool returns without any context, prerequisites, or 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?

No annotations exist, so the description carries the full burden. It does disclose the return payload ('ranked results with titles, summaries, and article paths') and that ranking is by relevance, which is genuine behavioral value. It says nothing about result caps beyond the schema default, pagination, latency, or auth requirements.

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 with zero filler, and the core capability is front-loaded before the return-format detail. Appropriately sized for a simple search tool.

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 usefully names the return fields, which is the main thing an agent would otherwise lack. However, it omits any mention of the category filter or result-limit behavior at the description level and gives no guidance on corpus scope or result handling, leaving it minimally viable rather than 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 100%, so all three parameters (query, category, maxResults) are already documented in the schema, including the enum-like category list and the default/max for maxResults. The description adds no syntax, operator, or matching-behavior detail beyond that, 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?

States a specific verb ('Full-text search') and resource ('Grandpa's AI research corpus'), and previews the return shape. It is clearly distinguishable from siblings like get_article or list_categories, though it never names an alternative 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 implied by 'search' — an agent knows to reach for it when it has keywords rather than an article ID — but there is no stated when-to-use/when-not-to-use guidance and no routing to get_article, get_latest_research, or get_topic_tree for other retrieval needs.

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