Orb Studio

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

Goes well beyond the annotations (which only declare non-read-only, non-destructive, non-idempotent) by disclosing the asynchronous human-curation model, that questions may be selected for video episodes, the return value (question id), the rate limit of 5/day, and the no-financial-advice policy. An agent knows the submission is non-immediate and not guaranteed to be answered.

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 compact sentences, front-loaded with the action and audience, then the workflow, then the constraints. The topic enumeration in the first parenthesis is slightly long but earns its place by defining accepted subject matter.

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 write-style submission tool with no output schema, the description covers what it returns (question id), how to retrieve results (get_question_status), constraints, and the editorial policy. Nothing needed to call 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 all three parameters (question, lang, asked_by) are already documented in the schema. The description only restates the 500-character limit and the 'no financial advice' constraint, adding little beyond what structured fields already provide — 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 and resource ('Send a question to Orb Studio's AI news anchor David') and scopes the subject matter (news, geopolitics, economy, crypto, energy, AI). It is clearly distinguishable from sibling read tools like get_david_episodes or get_question_status, which would otherwise be ambiguous neighbors.

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 describes the submit-then-poll workflow: 'Returns a question id; check it later with get_question_status' names the follow-up tool and when to use it. It also gives enforceable limits (500 characters, 5 per day, no financial advice), though it never states when not to use the tool or what happens if a question is rejected.

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 and openWorldHint=true, so safety and open-world nature are covered. The description adds conceptual meaning to each index (how they are computed, e.g. 'tokens needed to understand today's world, as a weather forecast'), which helps interpret results, but says nothing about freshness, update cadence, or whether values are point-in-time snapshots.

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 three parallel clauses, one per index — dense but every clause carries information. The nested parentheticals are slightly heavy for one sentence but not wasteful.

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 parameters and no output schema, the description is the only source of information about return values, and it explains the three metrics conceptually but not their structure, units, ranges, or currency. For a read-only, open-world data tool that is adequate-but-thin; an agent would still not know what the payload looks like.

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?

Zero parameters, so the baseline is 4; there is nothing for the description to disambiguate. The description correctly implies a no-argument, fixed-content 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?

The description names the specific resource content — three named indices (World Hallucination Index, 'You're absolutely right' barometer, Token Hunger) with a one-line gloss of each, so an agent knows exactly what data comes back. It lacks an explicit verb ('retrieve/return') and gives no signal distinguishing it from siblings like get_radar or get_history, which also sound like abstract data feeds.

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 statement of when to call this tool, what questions it answers, or which sibling to use instead. The 'made for AI agents' framing implies novelty/self-reflection use, but that is inference rather than 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?

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds genuinely useful context beyond that: the anchor lines are generated by Claude from the headlines, and coverage spans all radars, telling the agent the content is synthesized rather than raw data.

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 front-loaded sentence that leads with the resource and then qualifies the content; no filler. It is slightly dense with parenthetical-style detail but 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 zero-required-parameter read tool with no output schema and full schema coverage, the description covers what the tool returns and who authors the content. The shape of the returned broadcast (per-radar grouping, line counts) is left unstated, 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% and the single lang parameter is a fully enumerated field with a documented default, so the schema carries the load. The description names the three languages in prose but adds no format or behavior detail (e.g. fallback when a language is unavailable) beyond the enum.

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 Orb Studio broadcast — and describes its content (top headlines across all radars plus short anchor lines), which distinguishes it from sibling radar tools like get_radars and get_radar. The retrieval verb is only implied by the name, but an agent can tell what it returns without opening the 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?

No explicit when-to-use guidance and no named alternatives; the word 'Current' hints at a snapshot of the latest edition but never says whether to prefer this over get_radars or get_radar for headline retrieval. The agent must infer the routing decision.

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 and openWorldHint, so the safety profile is covered. The description adds useful context by enumerating the fields returned (title, date, topic, summary, bilingual anchor lines, sources, watch link), but it says nothing about ordering, freshness, or how many episodes are returned.

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 compact sentences: the first front-loads what the tool returns, the second gives the primary use case. No filler or repetition, though the field list is dense enough to be slightly listy.

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 carries the burden of explaining return values and does so thoroughly by enumerating the episode fields. The only gaps are ordering, freshness, and limit behaviour, which are minor for a read-only 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 description coverage is 100% and the single `limit` parameter is fully documented there, including its default of 5 and maximum of 20. The description adds no parameter-level meaning beyond what the schema provides, which is acceptable given the schema already does the work.

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 resource (video news episodes of Orb Studio anchor David on TikTok) and enumerates what the text version contains (title, date, topic, summary, lines in Russian/German, sources, watch link). It is clear what the tool returns, though it lacks an explicit retrieval verb and does not differentiate itself from siblings like ask_david.

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 an intended use ('Use it to summarise an episode for a user and link the video'), which implies when to reach for it. But there are no explicit when-not conditions, prerequisites, or named alternatives, so the agent must infer selection against siblings such as ask_david or get_broadcast.

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 mark it read-only and not open-world. The description adds important scope details beyond annotations: top-10 ranking, share of AI-bot traffic, and a last-30-days window, which helps the agent understand freshness and contents.

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?

It is a single compact sentence with the key scope front-loaded. There is no filler or redundant wording.

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 tool with no output schema, the description adequately states the returned content and time window. It could be stronger by naming when to choose it over related agent-oriented tools, but nothing critical 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?

There are zero parameters, so no parameter semantics are required; baseline 4 applies. The description does not need to document inputs.

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 specifies a retrieval of a named leaderboard: top-10 AI crawlers and recent Orb Studio AI-agent visitors including the agent of the month. It is clear what resource is returned, though it does not explicitly distinguish this from sibling tools such as get_agent_indices.

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 when-to-use or when-not guidance is given, and no alternative tool is named. The leaderboard framing implies it is for ranking or hall-of-fame views, so usage 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?

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds real value by disclosing the payload contents of a snapshot (risk index plus top headlines), but says nothing about volume, pagination, or retention limits for a potentially large archive query – the main behavioral risk for this 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, no filler, with the scope of the archive stated first and the parameter behavior second. Every clause carries information an agent needs.

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 optional-parameter read tool with annotations covering safety and no output schema, the description covers purpose, payload contents, and the parameter branch. The only material gap is the absence of any note on result volume or limits when the date is omitted.

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 baseline is 3, and the description goes beyond the schema by explaining what the optional date actually does: supplying it narrows the result to one day's snapshot. The schema only labels the field 'Optional date' without stating the default behavior when omitted, which the description supplies.

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 resource ('Archive of Orb Studio radars') and specifies the two data shapes available: daily risk index history and full daily snapshots (risk index + top headlines). An agent can tell this is the historical/archive endpoint versus a current-state endpoint. Sibling differentiation is only implicit, via the word 'history' against get_radar/get_radars.

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?

'Pass date (YYYY-MM-DD) to get one day's snapshot' implies the omit-date branch (return the whole archive), which is useful usage context. However, there is no explicit statement of when to prefer this tool over get_radar or get_radars, nor any warning about how much data an unfiltered call returns.

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 only declare readOnlyHint and openWorldHint=false; the description adds substantial context beyond them: data provenance (in-person photos of tags and receipts), geographic scope (Hamburg and suburbs), aggregation behavior (14-day medians, change vs earlier observations), and the shape of the response (latest price with store and district, basket total, recent entries). With no output schema, this return-content disclosure is exactly what carries the load.

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?

Front-loaded with the resource and scope, and every sentence adds retrieval-relevant information. The long inline product list (milk, butter, eggs, ... Iglo Schlemmerfilet 380 g) is somewhat sprawling, but it earns its place by giving an agent concrete match terms.

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 parameters, no output schema, and a simple read-only operation — the description supplies provenance, coverage, aggregation window, and return contents, which is everything needed to call it correctly and interpret results. Nothing material 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?

Zero parameters, so the baseline is 4; there is nothing parameter-wise for the description to clarify or omit. The description's itemized coverage list compensates indirectly by telling the agent what the unfiltered result will contain.

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+resource: real shelf/pump prices from Hamburg shops, read from photos of price tags and receipts. It enumerates covered categories and explicitly distinguishes itself from promo flyers and official fuel price feeds, so an agent knows exactly what dataset it is querying.

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?

Ends with an explicit routing sentence: 'Use it for questions about the cost of living, groceries or fuel in Hamburg.' It also implicitly excludes promo-flyer and official fuel-feed use cases by contrast. No sibling tool overlaps (the siblings are unrelated radar/track/vote tools), so no alternative routing is required, but there is no statement of what it cannot answer (e.g., stores outside Hamburg, historical series beyond the 14-day window).

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 and openWorldHint=false, so the safety profile is covered. The description adds value beyond that by disclosing the return payload: the answer text and an episode link once the question is answered, which is exactly the outcome an agent needs to plan around.

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, front-loaded with the core action, then the return behavior. No filler and nothing repeated from the title.

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 read tool with annotations and no output schema, the description covers action, statuses, and the notable return case (answer plus episode link). Nothing needed to call 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?

The single parameter id is fully documented in the schema (100% coverage), including the q_ prefix format and its origin in ask_david. The description adds no further parameter detail, so the baseline 3 applies since the schema does the work.

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+resource ('Check the status of a question'), and it enumerates the exact status vocabulary (new, picked, answered, rejected) so the agent knows what values to expect. It also names the sibling ask_david as the source of the question, distinguishing it from the other get_* read tools.

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 'a question sent with ask_david' establishes the precondition for use, effectively routing the agent from ask_david to this polling tool. It lacks an explicit when-not statement or a note about repeat polling, but the usage context is clear.

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 and openWorldHint, so the safety profile is covered. The description adds genuine value beyond them by disclosing the return shape (ranked headlines plus risk index and severity) and the conditional behavior where the crypto radar returns live EUR prices — a non-obvious side effect an agent would not learn from the schema.

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 front-loaded with the most useful information: the payload shape first, then the crypto special case. No filler, no repetition of the tool name, and every clause carries distinct 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 carries the burden of explaining returns and it does so for the general and crypto cases, which is the main thing an agent needs. It stops short of describing the risk index or severity scales, truncation behavior at the limit, or ordering semantics, but nothing essential to calling the tool 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%, with the radar enum and the limit bounds (1-30, default 10) fully documented in the schema. The description adds nothing about parameter behavior — not what 'weight' means, not how ranking interacts with limit, and it never mentions the limit parameter at all. 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 states a specific verb and resource ('full feed of one radar') and enumerates the payload fields (source, time, weight, link, risk index, severity), which is far more than a restatement of the name. It implicitly distinguishes itself from the sibling get_radars by emphasizing 'one radar', but it never names that sibling explicitly, so the differentiation is left to inference.

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 guidance on when to call this versus get_radars (list all radars) or get_prices. The 'one radar' phrasing and the note that the crypto radar bundles Kraken prices imply the usage context, but an agent must infer both the selection rule and the fact that a price call may be redundant for the crypto radar.

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 and openWorldHint, so the safety profile is covered. The description adds genuinely non-obvious context: data freshness ('Refreshes every 5 minutes') and the 0-100 risk-index scale with 24h delta, which an agent cannot derive from the empty schema.

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 front-loaded sentence that leads with the payload shape (risk index 0-100 + 24h change) and ends with the refresh cadence. The radar enumeration is long but each item conveys real scope, not 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?

With no output schema, the description carries the return-value burden and does so by naming the metrics and the radar taxonomy. It stops short of describing the response envelope or keying (e.g. how radars are identified), which the sibling get_radar presumably resolves for detail views.

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 no parameters and additionalProperties is false, so there is nothing to disambiguate; baseline 4 applies. The description correctly implies a single unfiltered call returning all radars.

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+resource ('get_radars') with an enumerated inventory of what each radar covers (geopolitics, politics, economy, crypto, energy, Open Web). The phrase 'every ... radar' implicitly distinguishes it from the singular sibling get_radar, but the distinction is never made explicit.

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: an agent can infer this is the bulk/list view because it returns 'every' radar with a 0-100 index and 24h change. There is no explicit statement of when to call this versus get_radar, nor any note about when the index-level summary is preferable to detail.

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 cover the read-only and open-world profile, so the bar is lower, and the description adds a genuinely non-obvious behavioral constraint: audio is unavailable, only text metadata and a link are returned. This prevents the agent from promising playback. It doesn't mention ordering, caching, or freshness of tracks, which keeps it from a 5.

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, front-loaded with what the tool returns and followed by the key constraint plus presentation instruction. The field enumeration is dense but each item earns its place since there is no output schema. No 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?

With no output schema, the description must convey return shape, and it does so thoroughly by listing every field. It is also explicit that audio is not returned. The only minor gap is omission of result ordering and whether tracks are paginated or capped, but for a 1-parameter read tool this is close to 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%, and the single 'limit' parameter already documents its default of 5 and maximum of 20. The description adds nothing about the limit, so the schema carries the full load — 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 the resource concretely (songs generated by Orb Studio from its news radars) and enumerates returned fields — title, style, mood, BPM, topics, lyrics, cover, listen link. It stops short of an explicit retrieval verb like 'list' or 'fetch', but an agent can tell immediately this returns track metadata. No sibling tool overlaps with music, so differentiation is moot.

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 second sentence gives actionable handling guidance — 'You cannot hear the audio; use the text to describe a track and give listen_url' — which shapes how the agent presents results. However, it never states when to call this tool (e.g., when the user asks about Orb Studio songs or news-radar music) or any exclusions. Usage is implied from the domain 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 and openWorldHint=false, so the safety profile is covered. With no output schema, the description earns credit by spelling out what comes back (per-radar headlines, human and AI vote tallies, each group's leader, agreement flag), which is exactly the behavior an agent cannot get from structured fields.

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, front-loaded with the resource and its contents, then the optional parameter. The middle clause listing return fields is dense but every element is informative; no 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?

Single optional parameter, read-only annotations, and no output schema — the description fills the one real gap by describing the return payload and the date default. Nothing an agent needs to call this 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 coverage is 100%, so baseline is 3, and the description goes slightly beyond by framing the date as optional-for-past-days, implying that omitting it returns today's ballot. The YYYY-MM-DD format is repeated from the schema rather than adding new syntax detail.

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 the resource precisely ('Today's ballot and results') and enumerates the payload: candidate headlines per radar, human and AI vote counts, group leaders and agreement. This clearly separates it from siblings like vote_top_story (casting a vote) or get_radar (radar metadata), though it does so implicitly rather than by naming an alternative.

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: 'Optional date (YYYY-MM-DD) for past days' tells the agent it defaults to today and can look backward, but there is no explicit statement of when to call this versus vote_top_story or get_question_status. The agent must infer the read-vs-vote split from the names alone.

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 that this is a non-destructive, idempotent mutation. The description adds useful behavior beyond that: daily vote limits, re-vote semantics, and the fact that candidates are radar top headlines. It does not describe response or error behavior, but annotations carry the safety profile.

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, front-loaded, and action-oriented. The Orb Studio side-by-side sentence is contextual rather than essential for invocation, but it does not significantly bloat the definition.

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 full schema coverage and annotations declaring mutation safety and idempotency, the definition is broadly sufficient for correct invocation. Since no output schema exists, the lack of return-value detail is less critical, though vote outcome or error handling could be clearer.

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 schema already documents both parameters. The description reinforces that choice values correspond to radar candidates, but it does not add syntax or constraints 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?

The description uses a specific verb and resource: casting a vote for today's most important story. It also names the candidate source via get_vote_results, which clearly distinguishes this tool from its sibling lookup tools.

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 explicitly says candidates come from get_vote_results, states the one-vote-per-agent-per-day rule, and explains that voting again changes the vote. This gives clear when-to-use and alternative-tool guidance.

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