Dictionary of Applied Machine Learning

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

With no annotations, the description does disclose useful behavior: the result includes abstracts for every term and `terms` are kept in the given order. However, it does not explicitly state that the operation is read-only, what happens when neither or both inputs are supplied, or how invalid topics are handled.

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 short and front-loads the returned value before explaining inputs. The trailing reference to a selection on alexjung.at/baukasten/ is somewhat cryptic and adds little for an agent.

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 no annotations, no output schema, and 0% schema description coverage, it covers the return content and both parameters reasonably well. It remains incomplete on tool-selection guidance and edge-case behavior, which matter 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 0%, so the description properly carries the burden: it explains `topic` as a path id or words from the title or audience, and `terms` as an explicit ordered list of term keys or names. It still does not clarify mutual exclusivity or precedence if both are provided.

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 that the tool returns one reading order together with the abstract of every term in it, which is specific enough to understand the resource and result. It only implicitly distinguishes itself from list_learning_paths by saying 'one reading order' rather than a list.

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?

Explains how to choose between `topic` and `terms`, but gives no guidance on when to use this tool instead of siblings such as list_learning_paths, lookup_term, or search_terms. The alternatives and selection conditions are left to inference.

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. It states the return contains cross-referenced terms and abstracts, but does not say whether the operation is read-only, how missing terms are handled, whether the result is ordered, or what permissions are required.

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 concise sentence with no wasted words, and it front-loads the key concept. It is slightly under-specified as a fragment rather than a complete usage-oriented sentence, but the structure is efficient.

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 one-parameter read tool, the description gives enough to understand the returned content (related terms plus abstracts). However, without annotations or an output schema, it still omits term format, empty-result behavior, and read-only expectations, leaving some gaps 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 description coverage is 0%, so the single parameter 'term' is undocumented in the schema. The description only weakly clarifies that 'term' identifies the entry whose See-also list is returned; it does not specify expected format, matching rules, or case sensitivity.

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 resource as the cross-referenced terms (See-also list) for a given entry, and adds that abstracts are included. It implicitly distinguishes from sibling tools like lookup_term and search_terms, though it does not explicitly name them or contrast use cases.

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 lookup_term, search_terms, or list_all_terms. The description implies the use case (retrieving See-also entries) but gives no explicit conditions, 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?

No annotations are provided, so the description carries the burden. It usefully discloses the returned fields (name and key) despite there being no output schema, but says nothing about result size, ordering, or pagination for what is presumably a full-catalog dump.

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 efficient sentence with no wasted words, front-loading the action and scope.

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 list tool with no output schema, the description is nearly complete because it names the returned fields. Only minor gaps remain around result volume and 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?

The tool takes zero parameters, so there is nothing to document and the baseline of 4 applies. The description correctly implies no filtering is required.

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 ('every term of the Dictionary of Applied Machine Learning'), and names the returned fields (name and key). It is distinguishable from siblings like search_terms or lookup_term by its list-everything scope, though it never names those alternatives 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 'list every term' framing: an agent can infer it should use this when it needs the complete term set. There is no explicit guidance on when to prefer search_terms or lookup_term for filtered or single-term access.

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 behavioral burden, and it does disclose the content shape: curated (i.e. opinionated author selection), one path per topic, each with audience and terms in reading order. It says nothing about whether the list is complete or paginated, or confirms that this is a side-effect-free read, which leaves real gaps for an unannotated 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, both load-bearing: the first describes what is returned and its structure, the second gives the when-to-use triggers. The parenthetical topic examples are the only elaboration and they directly help an agent match a user's subject area.

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, read-only listing tool with no output schema and no annotations, the description supplies both the return structure and the invocation triggers, which is enough to call it correctly. The only missing piece is any statement about completeness or pagination of the topic set, signaled loosely by the trailing ellipsis.

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 there is no parameter semantics to explain and the baseline of 4 applies. Schema coverage is 100% of an empty property set, and the description correctly implies a no-argument, list-everything call.

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?

It names a concrete resource — the author's curated reading orders, one per topic — and enumerates real topics (how ChatGPT works, AI foundations, explainable AI, mathematics of AI) plus what each entry carries (audience, ordered terms). The collection framing ('one per topic', plural orders) implicitly separates it from the singular get_learning_path sibling.

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 gives explicit trigger intents: 'Use when someone asks what to learn, read, or teach about a topic, or how to structure a course or seminar.' That is a clear, concrete usage condition rather than an implied one. It stops short of naming alternatives such as get_learning_path for a single path or lookup_term for a definition, so there are no 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 shape of the returned content (abstract, definition, cross-references), which helps, but says nothing about behavior for an unknown term (error vs empty result) or whether the read is side-effect free.

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 compact sentence with the resource and the returned fields front-loaded and no filler. Sized appropriately for a simple single-parameter lookup.

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-param, annotation-free, output-schema-free lookup, the description covers what it returns and how the term may be specified. The only meaningful gap is the not-found/error behavior.

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%, so the description should compensate. It does add that the term may be supplied as a key or a name, which is real meaning beyond the bare string schema, but it gives no guidance on accepted formats, casing, or ambiguity handling for the two modes.

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 action and resource: retrieves the full dictionary entry for a term, and enumerates the parts of the entry (abstract, definition, cross-references). This is clearly a single-term fetch, distinguishable from list_all_terms and search_terms, though it never names those 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?

The phrase 'by key or name' implies direct lookup rather than searching, which is useful, but there is no explicit statement of when to prefer this over search_terms or get_related_terms. Usage is only implied.

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 supplied, so the description bears the full disclosure burden. It mentions ranked 'best matches' but does not state read-only behavior, match scope, permissions, pagination or limit behavior, or what happens with zero results.

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 filler. It is appropriately concise, though a little more structure around search scope and ranking could improve clarity.

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, it states the core purpose and a basic return notion ('best matches'). But with no annotations, no output schema, an undocumented limit parameter, and no sibling routing, an agent lacks enough detail to invoke it confidently or choose among alternatives.

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 0% for both parameters. The description only implies that query is a keyword or phrase; it never explains the limit parameter, accepted query syntax, matching mode, or default result count.

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 ('Search') and resource ('term names and definitions') and indicates matching against a keyword or phrase. It is clear enough to distinguish a search operation in broad terms, but it does not explicitly differentiate itself from lookup_term, list_all_terms, or get_related_terms, 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?

It implies usage for keyword or phrase queries, suggesting a search context. However, it gives no explicit when-to-use guidance versus exact lookup, full listing, or related-term retrieval, and no exclusions are stated.

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