Rumors pick-up lines

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 usefully reveals that results include a 'literal English gloss', but it does not disclose ordering, determinism, error behavior, pagination, or the full response shape. This is adequate for a simple read operation but incomplete without annotation support.

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, focused sentences with no filler. The primary action is front-loaded, and the gloss detail is a valuable second sentence. Every phrase 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 plus schema is mostly enough to call the tool correctly: required collection URL, optional limit and register, and output content are all represented. Without an output schema or annotations, a bit more detail about return shape or ordering would make it fully complete, but the gap is minor for a simple collection-listing 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?

Schema coverage is 67%, with collection and register already documented in the schema. The description reinforces the collection-URL concept but adds no extra meaning for limit or register beyond what the schema's types, defaults, ranges, and descriptions already provide.

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 states a specific action ('Get lines') and a specific resource ('from one collection by its URL'), plus a non-obvious output trait: each line comes with a literal English gloss. This distinguishes it from siblings like search_lines and random_line, which imply different selection mechanisms.

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 conveys the core usage context: use this tool when you already know a specific collection URL and want its lines, rather than searching or picking randomly. It does not explicitly name alternatives or state when not to use it, so it misses the top level of guidance.

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 behavioral disclosure burden. It transparently states the operation is a list, the 'published' filter, and the exact output fields. It does not discuss pagination or response shape, but for a simple no-parameter read tool this is reasonably complete.

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 with no filler: it states the action, the resource, and the output fields. Every element earns its place and the key information is front-loaded.

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 names the output fields and the 'published' scope. It does not specify the response container or pagination, but nothing critical is missing for selecting and invoking this 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 the baseline is 4. The description adds useful output-field semantics (URL, title, language, line count) even though no parameter documentation is needed.

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'), a clear resource ('published pick-up line collections'), and enumerates the returned fields (URL, title, language, line count). This clearly distinguishes it from sibling tools that operate on individual lines or search them.

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 establishes the tool's purpose: to list published collections. It does not explicitly name alternatives or exclusions, but the context is clear enough that no prerequisites or competing conditions are needed for this no-argument list operation.

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 behavioral burden. It does disclose the essential behavior — one random result with optional narrowing — and 'random line' implies a non-destructive read. But it does not state what happens when filters match nothing, whether repeated calls can repeat lines, or what the returned value looks like. For a very simple tool the core behavior is covered, but edge behavior is left undisclosed.

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 sentence that front-loads the core purpose ('One random line') and appends the scoping info ('optionally narrowed by language and register') behind it. Zero wasted words; every token contributes to an agent's decision to call this 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?

For a tool with only 2 optional parameters, no output schema, and no annotations, the description is nearly adequate: it conveys the operation and the filtering behavior. It is incomplete on the return format (no output schema to fall back on), the source corpus, and the meaning of 'register', which an agent would need to know 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 coverage is 50% — lang is documented as a Base language subtag but register is not. The description adds real semantic value by clarifying that both parameters are optional narrowing filters rather than required inputs. However, it provides no format, allowed values, or meaning for 'register', and compensation for the undocumented register parameter is limited.

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 states the core operation — returning a single random line — with an implicit verb that is recoverable from context. It names the resource ('line') and its defining trait ('random'), which distinguishes it from the sibling tools get_lines, search_lines, and list_collections. However, it never says what corpus/collection the line is drawn from, so an agent cannot tell the source without opening a sibling's 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?

The phrase 'optionally narrowed by language and register' weakly implies a filtering use case, but there is no explicit statement of when to choose this over get_lines or search_lines, and no exclusion criteria or conditions are given. The agent is left to infer that 'random' is the distinguishing use case on its own.

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 burden. It does disclose meaningful behavior: case-insensitive substring matching across three specific text fields. However, it does not mention result ordering, pagination, empty-result behavior, or any access or rate-limit considerations, so there are clear gaps.

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, front-loaded sentence. Every word adds meaning: case-insensitive, substring, and the three searched fields. There is no redundancy or 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 simple search tool, the description covers the core matching behavior, but with no annotations and no output schema it should also clarify the role of lang, mention that matching lines are returned, and ideally point to alternatives. It is adequate but not 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 0%, so the description must compensate, but it only adds meaning to the query parameter by indicating which fields are searched. It provides no extra semantics for the lang or limit parameters, which are left entirely to the schema's default/min/max clues.

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 states a specific verb ('search'), a clear resource (lines), and the matching scope (native text, transliteration, English gloss). It distinguishes the tool from get_lines and random_line by describing a search behavior, though it does not explicitly contrast it with 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 description implies when to use the tool: when a case-insensitive substring search across those fields is needed. However, it gives no explicit guidance about when not to use it or which sibling alternative to prefer, leaving the usage context implicit.

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