Shakespeare's Monologues

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so safety is covered. The description adds genuinely useful behavior beyond that: the response is a catalogue entry rather than the monologue text, and the full text lives behind the returned `url`. That return-shape disclosure is the main value-add.

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 no filler; the retrieval action and key are front-loaded and the follow-up pointer to full text comes second. Nothing is wasted.

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 read-only lookup with no output schema and fully documented parameters, the description covers the essentials: what is returned (catalogue entry) and how to get the underlying text (url). It is slightly thin on relationship to siblings, but nothing needed to invoke it correctly is missing.

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 every parameter (id, context, llm_model, deprecated monologue_id, conversation_id) is already documented in the schema. The description only restates that `id` is numeric and sourced from search_monologues, adding little beyond the schema, 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?

Specific verb (Fetch) plus resource (one monologue's catalogue entry) and the key retrieval key (numeric id). It also implicitly distinguishes the catalogue record from the full text, which separates it from siblings like get_paraphrased_monologue and search_monologues. No sibling is named explicitly, 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?

Usage is implied by 'by its numeric id' and the schema note that the id comes from search_monologues, but the description itself offers no explicit when-to-use or when-not-to-use guidance against siblings such as get_monologue_of_the_day or random_monologue. The only concrete guidance is the follow-the-url instruction for retrieving full text.

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 readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds only the semantic hint that the content is the most recently social-posted piece (implying freshness), but says nothing about caching, auth, or staleness beyond that.

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 that defines the returned resource with zero filler. Nothing extraneous and nothing that could be trimmed without losing information.

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 getter with no output schema, the description adequately conveys what is returned (the current daily featured monologue). It is nearly complete, missing only return-shape or freshness details that would fully specify the payload.

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 are fully documented in the schema (context, llm_model, conversation_id are analytics plumbing). The description adds no parameter meaning, which is acceptable since the schema carries the full burden — baseline 3.

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 resource — the current 'Monologue of the Day' — and clarifies it as 'the piece most recently posted to the site's social feeds,' which distinguishes it from random_monologue and get_monologue. The retrieval verb is implicit rather than explicit, so it falls just short of the top mark.

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 describes the content but gives no when-to-use guidance and names no alternatives, despite siblings like random_monologue, search_monologues, and get_monologue that an agent must choose between. The selection condition is left entirely 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?

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds genuinely useful behavior beyond that: the paraphrase is AI-generated (Claude), may be null if not yet generated, and the url field always carries the monologue — telling the agent how to handle a partial response.

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; the returned content is front-loaded and the null/url caveat follows as the second most important fact. 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?

For a read-only fetch with no output schema and full schema coverage, the description covers the key unknowns: what comes back and how to cope with a missing paraphrase. It stops short of any guidance on sibling selection, which is the only real gap.

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 every parameter (id, context, llm_model, monologue_id, conversation_id) is already documented in the schema, including the analytics-only context/llm_model and the deprecated alias. The description adds no parameter-level meaning beyond that, so the 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?

States a specific verb (Fetch) and resource (monologue) and names the two things returned: full text plus AI-generated line-by-line paraphrase. This implicitly separates it from get_monologue, but it never names that closest sibling explicitly, so an agent must infer the boundary.

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 — the paraphrase content signals when to reach for this over get_monologue, and the note that the paraphrase may be null hints at a fallback. There is no explicit 'use this when / use X instead' guidance despite get_monologue being an obvious alternative.

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 cover the safety profile (readOnlyHint, destructiveHint=false, closed world), so the bar is lower. The description usefully adds that the summary is AI-generated and may be absent/null if not yet generated, which is real behavioral context beyond the 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?

Two short sentences, front-loaded with purpose and followed by the null caveat. No filler or redundant restatement of the name 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 read-only lookup with a fully documented schema and no output schema, the description is nearly complete: purpose, example inputs, and the null-return possibility. Only the match semantics and alternative tools are left implicit.

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 all four parameters, including the play title being exact or partial. The description's examples ('Hamlet', 'The Tempest') add only marginal illustrative value, matching the baseline 3.

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 (Fetch) and resource (AI-generated summary of a play) and gives concrete examples ('Hamlet', 'The Tempest'). The 'of a play' scope implicitly separates it from the sibling get_scene_summary, but no sibling is named 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?

The description gives no when-to-use guidance and never names an alternative such as get_scene_summary or list_plays. 'May be null if not generated yet' is a return-value caveat, not usage direction, so the agent must infer when this tool is appropriate.

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 readOnlyHint=true, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds genuine value beyond that by disclosing the return behavior ('May be null if not generated yet') and that the summary is AI-generated.

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 action and resource, with the nullability caveat last. No wasted words.

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 tool with no output schema and full annotation coverage, the description adequately conveys what is returned and its possible null state. It does not explain the required context/llm_model analytics params, but those are documented in the schema.

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 no parameter-level detail (e.g. that id comes from search_monologues), leaving the schema to carry all parameter meaning.

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: 'Fetch an AI-generated summary of the scene a monologue appears in.' The scene-level scope implicitly distinguishes it from get_play_summary, though no sibling is named 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?

The parenthetical '(context for the speech)' implies the intended use case, giving implied guidance. However, it never states when-not to use it or names alternatives like get_play_summary or get_monologue.

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 readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds genuinely useful behavior beyond that: matching is exact-first, then substring fallback. It omits return ordering, pagination behavior, and result limits (though limit lives in the schema), so it goes only modestly beyond 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?

Two sentences, zero waste, with the verb-resource purpose front-loaded and the matching behavior as a tight second sentence. Nothing could be cut without losing information.

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 does not need to explain return values, and annotations carry the safety profile. It covers purpose and matching adequately; only minor gaps remain around result ordering and pagination, which the limit parameter partially addresses.

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 five parameters are documented in the schema, establishing the baseline of 3. The description adds only indirect character-matching semantics (exact then substring) and no syntax or format detail beyond what the schema already provides.

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 ('List every monologue spoken by a given character') with concrete examples, which clearly separates it from random_monologue and get_monologue_of_the_day. It stops short of distinguishing itself from the near-neighbor sibling search_monologues, so it does not fully earn 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?

The tool name plus description imply when to use it (enumerate a character's monologues), and the matching note hints at input tolerance. However, there is no explicit guidance on when to prefer this over search_monologues or get_monologue, and no stated 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?

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds real behavioral value by disclosing what the response contains (classification and monologue counts), which matters because there is no output schema; it stops short of mentioning ordering or whether the list is paginated.

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 no filler; the verb and resource come first and the qualifiers follow. 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 read-only listing tool with annotations covering safety and a description covering the return contents, this is nearly complete. Minor gaps remain around ordering/pagination and whether the list is exhaustive, but an agent can call it correctly as written.

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 three parameters (context, llm_model, conversation_id) are fully documented in the schema itself. The description adds no parameter meaning, which is acceptable given the parameters are analytics plumbing rather than domain inputs — 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 ('List') and resource ('Shakespeare's plays') and specifies the payload contents (classification, monologue counts). This distinguishes it from get_play_summary and search_monologues, which return detail or filtered subsets rather than an enumeration of all plays.

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 enumeration framing implies when to reach for it (browsing/discovering the corpus), but there is no explicit guidance about when to prefer it over siblings like search_monologues or get_play_summary, and no exclusions stated. Usage is inferable 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?

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds only the fact that results are random and filterable; it does not disclose whether repeated calls can return the same monologue or how the pick is bounded.

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 with no waste; the core action is front-loaded and the usage hint follows immediately. Nothing needs trimming.

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 random-selection tool, the description covers what it does, how to narrow it, and what it returns. With no output schema, it correctly indicates a single monologue is returned, though it says nothing about the monologue's shape or metadata.

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 play, gender, context, llm_model, and conversation_id are already fully documented in the schema. The description's "optional gender/play filters" merely restates that, adding no syntax or format detail. 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 ("Return one random monologue") plus the optional narrowing filters. The word "random" implicitly separates it from get_monologue, get_monologue_of_the_day, and search_monologues, though no sibling is named 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?

"Useful for a suggestion when the user is undecided" gives a clear situation that selects this tool over the deterministic or search-based siblings. It stops short of naming an alternative or stating when not to use it, so it is not a full 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?

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description still adds non-redundant behavior: it discloses the shape of the return payload (matches, each with a permalink `url`), which is the only place an agent learns this since there is no output schema. It says nothing about pagination limits or the conversation_id handshake, but the core behavioral disclosure is present.

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 the core capability front-loaded and the return-format note second. No filler, no restatement of the title or name, and every clause carries information.

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 nine-parameter search tool with full schema coverage and no output schema, the description covers what the tool does, what it matches on, and what comes back. It omits any note about the required context/llm_model analytics params and the conversation_id continuation rule, but those are fully specified in the schema, so the gap is minor.

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 query, act, play, style, gender, limit and the analytics params. The description's mention that free text matches character, play, or first line merely restates the `query` schema text, and "optional filters" adds no detail beyond what the properties already specify. 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?

States a specific verb and resource ("Search Shakespeare monologues") plus the matching scope (character, play, first line) and the presence of optional filters. An agent can distinguish it from get_monologue or list_all_monologues_for_a_character, though the description never names a sibling to sharpen the boundary.

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: search when you want to find monologues by free text. There is no explicit when-to-use/when-not guidance and no mention of the sibling alternatives (get_monologue for a known ID, list_all_monologues_for_a_character for enumeration, random_monologue for discovery), so routing relies on 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=true, so the safety profile is covered. The description adds real behavioral context beyond that — a scope restriction and a privacy constraint ('Do not include personal information about the user') — but says nothing about what happens to the feedback afterward, whether a submission is confirmed, or any rate limiting. There is mild tension between the verb 'send' and readOnlyHint=true, though submitting feedback to a feedback channel does not modify the monologue corpus, so it is not a contradiction.

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?

Three sentences, front-loaded with the core action, then the scope bound, then the privacy constraint. No filler, no restatement of the tool name, and each sentence adds a distinct constraint.

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 100% schema coverage, no output schema, and a flat 10-parameter object, the description does not need to explain parameters or return values. It covers the two things the schema cannot — scope and privacy — leaving only minor unstated items such as post-submission confirmation or rate limits.

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 ten parameters — including the nuanced conversation_id reuse rule and the 'pass "unknown" rather than guess' instruction for llm_model — are already fully documented. The description only restates the feedback_type categories in prose and adds no syntax or format detail the schema lacks. Baseline 3 applies when structured data carries the whole burden.

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 ('Send feedback about this Shakespeare monologues server') and immediately enumerates the acceptable subject matter: a missing monologue/play/capability, a problem with a result, or praise. Combined with the sibling list of purely read-only retrieval tools, an agent can tell instantly that this is the sole outbound/write-channel tool.

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?

Explicitly bounds usage: 'Only for feedback about these tools, not about other tasks' gives a clear when-not, and the feedback_type schema enumerates when to pick missing_capability vs issue vs praise vs other. This is about as close to explicit when/when-not/alternative routing as a feedback tool can get.

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