Digital Maker AI

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

With no annotations, the description carries the full burden and does so richly: it discloses the credit model (spends animated scenes allowance, Premium 5/Ultimate 10 plus Animation Pack, never video minutes), the duration (a few minutes), and the concurrency limit (one animation at a time per video). It also confirms the original video is preserved, which is critical for a mutation 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?

Three sentences, front-loaded with the action and outcome, then constraints, then cost and timing. Dense but slightly repetitive in restating the scene-selection requirement.

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?

Despite no output schema, the description closes the loop by directing the agent to check_animation for the resulting link, and it covers cost, eligibility, concurrency, and sequencing. An agent has everything needed to call this correctly and explain the outcome to the user.

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 both parameters are already documented, including that scene numbers come from list_video_scenes and start at 1. The description reinforces the 'which scenes, in their own words' framing but adds no format or syntax detail beyond the schema.

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 ('Turn chosen scenes of a finished video into moving AI video clips') plus the side effect of rendering a new animated video alongside the kept original. An agent can distinguish it from siblings like create_video or check_animation without opening a 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?

Explicit when/when-not: multi-scene only, landscape or vertical, not whiteboard or single scene. It also gives the sequencing rule (only call after the user has named the scenes in their own words) and routes the agent to check_animation for the link.

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 behavioral burden and does so well: it discloses the cost model ('Free, with a daily limit'), the state dependency (unmade draft from draw_presenter), the identity-preservation guarantee, and that the result is shown. It omits what happens to the original image or error behavior when the draft is already made.

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?

Four short sentences, each load-bearing: purpose, scope, output, cost/limits, and precondition. Front-loaded with the action, no filler or restated 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?

No output schema and no annotations, so the description must stand alone, and it covers purpose, output ('shows the new picture'), cost, limits, and state constraints. Missing only edge-case behavior such as failures or what happens to the prior image.

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, but the description goes beyond the schema by enumerating the changeable dimensions (background, outfit, hair, glasses, expression) and reinforcing the 'one thing only' constraint. That extra framing meaningfully guides how to fill the single 'change' parameter.

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 ('Change one thing about the presenter picture just drawn') and immediately scopes it ('keeping the same person'). It is clearly distinguishable from draw_presenter, make_presenter, and check_presenter because it operates on an existing draft rather than creating or inspecting one.

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?

Gives an explicit precondition: 'Only works on an unmade draft from draw_presenter,' which routes the agent to the right sibling when no draft exists. It does not spell out the negative case (e.g. what to call when the draft is already made) beyond the constraint, so it stops short of full when/when-not coverage.

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 (readOnly, idempotent, non-destructive), and the description adds context beyond them: the call is a status probe whose result becomes richer over time (the link appears 'once it is ready') and it should not be polled. That timing/polling behavior is not expressible in annotations and is the main thing an agent could get wrong.

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 with no filler; the return content is front-loaded and the usage constraint follows. Minor awkwardness in 'is getting on' but nothing 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?

With no output schema, the description does the necessary work of saying what comes back (progress status and, eventually, the animated video link). Combined with annotations and full param coverage, an agent has enough to call 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?

Only one parameter and schema description coverage is 100%, including the 'omit for the most recent video' default, so the schema carries the load. The description adds nothing about video_id semantics beyond that, which is the expected baseline when coverage is complete.

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 (an animation created by animate_scenes) and what it returns: progress plus the link to the animated video once ready. It clearly differs from siblings like check_video or check_drive_upload, though the phrasing 'How ... is getting on' is informal rather than a crisp verb+resource statement.

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?

Gives an explicit trigger ('Call it when the user asks') and an explicit anti-pattern ('not on a loop'), which is genuinely useful routing guidance. It does not name a specific sibling alternative, but the on-demand vs polling distinction is the key decision here.

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 indicate readOnly and idempotent behavior. Description adds typical timing (under a minute, longer for long videos) and advises against polling, which is useful behavioral context beyond annotations. But doesn't describe error states, what happens if job_id omitted, or return format.

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 concise sentences, front-loaded with purpose and timing, then usage guidance. Every sentence adds value with no redundancy.

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 check with one optional parameter and no output schema, the description covers purpose, timing, and usage. Minor gap: doesn't explain what the Drive link looks like or handling of errors, but adequate for basic use.

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 parameter semantics are fully documented in the schema. The description doesn't add additional meaning about job_id, but doesn't need to since schema is complete. 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 specific verb (Check) and resource (Drive upload progress), plus outcome (get Drive link). Distinguishes from siblings like save_to_drive and check_video by naming the specific upload job.

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?

Provides clear context: call when user asks, not on a loop. Implicitly suggests polling is discouraged. However, doesn't explicitly name save_to_drive as the prerequisite or alternative for checking other jobs.

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, idempotentHint, and destructiveHint=false, so safety profile is covered. The description adds useful guidance against polling, which is a behavioral trait not in the annotations. It doesn't mention what 'ready' means or what the response looks like.

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 purpose and the usage warning are front-loaded. Every sentence 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 simple one-parameter read-only status check with full schema coverage and annotations, this is adequate but thin. No output schema exists, so the description could say more about what 'ready' means or the shape of the result, which an agent would need to interpret the response.

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% and the single parameter 'name' is fully documented in the schema, including the omit-for-most-recent behavior. The description adds no parameter meaning beyond the schema, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States clearly it checks whether a presenter from make_presenter is ready for video use, naming the verb (check readiness) and resource (presenter). Distinguishes itself from make_presenter by referencing it, though it doesn't differentiate from sibling status-checkers like check_video or check_animation.

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?

Gives a temporal cue ('yet') and a behavioral instruction ('call when the user asks, not on a loop'), which implies polling avoidance. However, it doesn't specify which alternative to use when the user asks about other readiness states, and doesn't clarify prerequisites like whether make_presenter must have been called first.

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 read-only and idempotent behavior, so the description adds genuinely new context: the tool becomes useful only after posting, returns links once complete, and should not be polled. The anti-loop warning is a behavioral trait not expressible in 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 the return semantics and closed with the usage constraint. No filler, no restatement of the name.

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 telling the agent what comes back, and it does ('the links once it has'). What's missing is the intermediate state (what it reports while the post is still pending), but the core contract is covered.

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?

One required parameter with 100% schema description coverage, so the schema already explains that post_id comes from post_to_social. The description reinforces the origin but adds no format or syntax detail beyond it; 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 check verb against a specific resource (a post from post_to_social) and says what it yields: whether the post went out plus the links. An agent can distinguish it from the sibling post_to_social, which publishes rather than reports.

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 says when to call it ('when the user asks') and, more importantly, when not to ('not on a loop'), which is real operational guidance. It doesn't enumerate alternative status-check siblings, but the reference to post_to_social grounds the workflow.

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 readOnly/idempotent/non-destructive, so the safety profile is covered. The description adds genuinely useful operational context the annotations cannot: long-form jobs take many minutes (latency expectation) and should not be polled in a loop. It doesn't state behavior on an invalid or expired video_id.

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 short sentences, front-loaded with the action and outcome, then the timing caveat, then the invocation rule. No filler; every sentence changes agent behavior.

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?

There is no output schema, so the description partially compensates by promising the link once ready, and it sets latency expectations for long-form jobs. It could say more about the not-ready-yet case (e.g., what a pending response looks like), which is the main remaining gap for a status-polling 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 100% and the single parameter is fully documented there ('Job id from create_video. Omit for the most recent video.'). The description adds nothing about the parameter, 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 and resource ('check how a video is coming along') plus the payoff ('get the link once ready'), which cleanly separates it from the other check_* siblings (check_animation, check_presenter, check_social_post, check_drive_upload). An agent can route to it 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?

Explicitly says when to call ('when the user asks') and when not to ('not on a loop'), which is exactly the failure mode for a polling-style status tool. It stops short of naming an alternative tool or stating what to do if the job isn't ready yet.

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

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

No annotations are provided, so the description carries the full burden and does so: it discloses the fixed rendering contract (full-body, white background, identical in every scene), the cost (one image from the monthly allowance), the account limit (10 characters), the plan gate (Pro and above even though faceless video needs Premium), and the side effect of landing in the image library. It also sets the expectation to show the result and offer a redo.

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 purpose and the sibling disambiguation before the procedural detail, and every paragraph covers a distinct concern (rendering contract, reference-image workflow, cost/plan). It is long and slightly repetitive around the reference-image handling, but no sentence is 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?

There is no output schema and no annotations, so the description must supply everything an agent needs before calling: what gets created, how it looks, what it costs, plan eligibility, limits, and what to do with the returned character afterwards. All of that is present.

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% and the schema already documents all three parameters, including the style enum mapping, so the baseline is 3. The description adds real value beyond the schema by specifying what to put into the description parameter when a reference image is shared (archetype, art style, palette, build, hair, clothing, vibe) and by reinforcing the uniqueness requirement on name.

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 ('Create a reusable character ... save it to their Digital Maker account, from a written description') and immediately contrasts with the sibling draw_presenter for talking hosts. The 'drawn INTO the scene pictures' clause disambiguates it from presenter/avatar tools an agent might otherwise confuse it with.

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?

Explicit when-to-use (a character drawn into scenes), explicit when-not (talking on-screen host -> draw_presenter), plus the downstream workflow (pass the name to create_video). It also gives a concrete procedure for the reference-image case, including what to ignore (framing, background) and what not to do (reproduce a real person's face).

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 burden and does so: it warns 'SPENDS REAL MONEY' with the exact cost model (minutes from the monthly pool, Premium 30 / Ultimate 60 caps), discloses the async behavior (returns a job id at once), states hosts' voices must differ, that only built-in voices (not clones) are allowed, and that podcasts have no background music.

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?

Long, but the highest-stakes information (the money warning and the once-per-request rule) is front-loaded and the rest is dense prerequisite/lifecycle guidance rather than filler. For a 14-param tool with nested hosts and multiple dependencies, the length is largely earned; a slight trimming pass on follow-up tooling would help.

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 the complexity (14 parameters, nested host objects, no output schema, no annotations), the description covers cost, async return, prerequisites, constraint on voices, show inheritance, and the post-generation workflow. An agent has everything needed to call it correctly without opening other tools.

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 79% (baseline 3), and the description adds real meaning on top: it explains the three legitimate sources for studio (approved preview image_url, saved-studio name, or public link), the origin of voices (list_video_options built-ins), and the show_name/save_show inheritance behavior. Only minor gaps (e.g., like_cta/comment_cta/subscribe_cta semantics) remain undocumented.

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?

Opens with a concrete verb+resource ('Start an Animated Podcast episode') and specifies the distinguishing shape: two AI hosts, two different voices, a topic, a held studio picture, captions. It is clearly separable from create_video and animate_scenes, and it names its follow-up tools (check_video, edit_podcast) for lifecycle clarity.

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 states when and how to call: agree topic/length/tone/hosts/studio with the user first, 'call once per request on their own direct request only.' It routes to the correct prerequisite tools (preview_podcast_studio, list_podcast_studios, list_video_options) and to check_video for progress, leaving little 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?

With no annotations, the description carries the full burden and discharges it: it discloses that it returns immediately with a job id, rendering takes many minutes, it SPENDS pool minutes one-per-minute (and vertical costs a flat minute), presenter screen time is billed from avatar time not video minutes, cloned voices use ElevenLabs credits, and vertical is capped at 5/24h. It also corrects a likely misconception (stills slideshow, not animation).

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 core purpose, then organized under clear headers (MUSIC, AI PRESENTER, OWN VOICES, ORIENTATION, STILL IMAGES). It is long, but with 34 parameters and real cost/prerequisite stakes just about every sentence earns its place; a small amount of redundancy (e.g. vertical constraints restated) keeps it short of a 5.

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 34-parameter, high-stakes creation tool with no annotations and no output schema, the description is remarkably complete: it covers cost, format constraints, prerequisites (preview_single_image, list_presenters), cross-tool handoffs, and the still-vs-motion limitation. Nothing an agent needs to call it correctly appears to be 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 the baseline is 3, but the description adds cross-parameter semantics the schema alone doesn't emphasize: orientation's flat-minute pricing, duration_seconds vs duration_minutes, the whiteboard duration caps, and the presenter/avatar-time cost interaction. It adds genuine meaning beyond the per-field schema text.

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 ('Start a faceless video on Digital Maker AI') and immediately enumerates the three formats (multi, single, whiteboard), so the agent knows exactly what the tool produces. It is clearly distinguishable from siblings like create_podcast, generate_image, and animate_scenes.

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?

Explicit when/when-not/alternatives: call list_video_options first if unsure, agree the format/topic/length/style/voice with the user before calling, never trigger on shared documents, never call twice for the same request, and use animate_scenes afterward for motion. This is close to best-in-class routing 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 present, the description carries the full burden and does so: drawing is free, redone-able, subject to a daily limit, replaces any unmade draft, is gated to Premium/Ultimate, and always produces a photoreal seated-at-desk image. These are exactly the operational facts an agent needs before invoking a generation 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?

Front-loaded with purpose and workflow, then constraints and gating. It is on the long side at roughly seven sentences, but each sentence carries distinct information (workflow, cost/limit, input guidance, prohibition, fixed output framing, plan requirement) rather than restating the 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 one-parameter generation tool with no output schema and no annotations, the description supplies everything needed: pipeline position, cost and limit behavior, draft replacement semantics, plan gating, input guidance, and the fixed visual framing. 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?

Schema coverage is 100% and already documents the single 'description' parameter including its length bound, so baseline is 3. The description goes beyond the schema by enumerating what to include (age, build, hair, clothes, expression, room) and by stating the fixed pose that the input cannot change, which meaningfully constrains how the parameter should be written.

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 ('Draw a new AI presenter') and immediately differentiates itself from siblings by naming change_presenter and make_presenter and situating itself as 'step one' of a three-tool workflow. An agent can place it in the pipeline without opening any other 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?

Explicit sequencing: draw, then adjust with change_presenter, then make_presenter. It also states an exclusion ('Do not describe a real identifiable person') and the retry condition ('calling this again replaces any unmade draft'), so both when-to-use and when-not-to-use are covered.

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 delivers: 3-update cap, non-destructive versioning (current version stays until new one is ready), re-record vs re-render semantics for different change types, a 10% length constraint, and that it spends no minutes. This is exactly the beyond-structured-fields context an agent needs.

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-loads the core action and drops constraints in a dense, mostly sentence-per-idea block with no filler. It is somewhat long and has a slightly run-on opener ('as the editor under the podcast on the site does'), but nearly every clause conveys a distinct operational fact.

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 complex, high-parameter mutation tool with no output schema and no annotations, the description covers the safety, cost, versioning, and limits an agent must know before calling. Nothing essential to correct invocation 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 67%, and the description reinforces the parameter groups by mapping edit intents to fields ('rewrite, delete or reassign script lines' → line_changes, 'either host's voice or name' → host_*_voice/name, 'captions, sound bar or studio picture'). It adds contextual meaning but does not detail individual optional params beyond what the schema already documents.

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 ('Change a FINISHED Animated Podcast') and enumerates what can be edited (script lines, host voice/name, captions, sound bar, studio picture), which cleanly separates it from siblings like create_podcast, get_podcast, and preview_podcast_studio. An agent can identify the tool 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?

Gives clear preconditions ('FINISHED' podcast, up to 3 updates, agree exact changes first and call once) that direct when and how to invoke it. It implies but never names alternatives or explicitly states when not to use it (e.g., use get_podcast to read first, create_podcast for new ones), so it stops short of full routing 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 readOnly/idempotent/non-destructive, but the description adds context none of them carry: the database is continuously updated so repeat calls return fresh view counts and earnings, the style tag lists are hand-curated and small so counts should be reported honestly, and calls are free. It also hints at returned fields (niche, size, cadence, earnings), which no output schema exists to supply.

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?

Four short blocks, each doing real work, with purpose and answer-from-this-data instruction front-loaded. Slightly long for the payload — the limit-counting guidance and the 'reads only, costs nothing' line partly restate the schema and annotations — but no block is 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 an 8-parameter tool with no output schema, the description covers when to invoke, when to re-invoke, how the style taxonomy maps to requests, the limits of curated data, and roughly what comes back. There is no evident gap an agent would need to guess at before calling.

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 a baseline of 3 is warranted, and the description genuinely exceeds it by defining what each video style actually looks like ('animated podcast' = illustrated or AI hosts at a podcast desk, '3d animation' = Pixar-style CGI), which the bare enum values do not convey. It also justifies max_subscribers as a way to find modelable smaller channels and instructs that limit should match what the user asked for, though these partly echo the schema text.

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?

Opens with a specific verb and resource ('Search Digital Maker AI's Faceless Finder database') and immediately characterizes the data: real YouTube channels confirmed as faceless, with niche, size, cadence and earnings. This is trivially distinguishable from every sibling tool, which are all creation/edit operations rather than a research lookup.

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?

Enumerates the triggering intents explicitly: asking about faceless channels in a space, wanting examples to model, asking who is winning a niche, asking what a channel earns, or naming a channel to look up. It also names the alternatives it should displace — the model's own YouTube recall and a previous answer already in the conversation — and tells the agent to re-call on refresh requests.

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 burden and does well: it discloses quota costs (Pro 200 / Premium 500 / Ultimate 1,000 per month), an hourly rate limit (100 images/hour), and that results persist to the user's library. It omits failure/moderation behavior and whether the image is returned inline versus only saved, keeping it short of 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?

Purpose, quota, rate limit, and the image_type warning are front-loaded before the sibling routing, which is the right order. It is a long description, but nearly every clause carries actionable information; a couple of sentences (quota enumeration, the looping warning) run long and could be tightened.

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 3-parameter generation tool with no output schema, the description supplies everything needed: cost, limits, persistence to library, the critical image_type constraint, character requirements, and explicit sibling routing. Nothing an agent needs 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 the baseline is 3. The description goes beyond the schema by warning that image_type 'controls the shape of the result' and that choosing wrong yields unusable output, and by clarifying that character must already exist and that its face will match. That is genuine added meaning over the enum docs.

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?

Opens with a concrete verb+resource ('Generate a ... image') and immediately enumerates the artifact types it covers. It also explicitly demarcates itself from siblings, naming create_character and draw_presenter as the correct tools for adjacent tasks, so an agent can disambiguate without opening a 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?

Gives explicit when/when-not routing: use create_character to CREATE a reusable character, use draw_presenter for AI presenters/avatars, and use this tool only when featuring an existing saved character. It even constrains call volume ('never loop to produce dozens of variations unless they asked').

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 readOnly and idempotent; the description adds that the call is a cost-check and implicitly free/non-mutating. It does not describe response shape, but readOnly annotations carry that 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?

Two sentences, front-loaded with what is returned, then the when. No waste.

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-param read-only preflight tool this is essentially complete. Return values are not enumerated field-by-field, but the description names the key fields and no output schema exists, so a small gap remains.

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 baseline is 4; nothing to document and the description correctly treats it as parameterless.

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 exactly what it returns: plan, renewal date, video minutes remaining, Drive connection status. Specific and distinguishable from every sibling, none of which expose account/billing state.

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?

Gives a concrete trigger: 'Check before starting a long video so the user knows the cost.' No explicit when-not, but the preflight-before-video condition is actionable and names the alternative path implicitly.

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 does well on the highest-risk point: 'Nothing is charged unless the user opens the link and pays' and 'They never expire' tell the agent this is a non-destructive, non-committal action. It omits how the link is returned, whether it is single-use, or any auth/account coupling.

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 tight sentences, front-loaded with the purpose, followed by the billing reassurance and the pricing tiers. Every clause carries information the agent or user needs; nothing is padded.

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 purchase-link tool with no output schema, the description covers purpose, trigger, pricing, and the no-charge guarantee. It does not describe what the caller receives back (presumably a URL) or whether the link is tied to the requesting account, which is the remaining 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 coverage is 100%, so the baseline is 3, but the description goes beyond the bare 'Which pack.' schema text by mapping each enum value to its scene count and price (starter 5 scenes $9, creator 11/$19, studio 43/$69), which helps the agent pick the right tier. The optional video_id is left to the schema's own explanation.

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 deliverable — a secure checkout link for an Animation Pack — and states the triggering situation (the user wants more animated scenes than they have left). That condition implicitly separates it from the animate_scenes sibling, which consumes scenes rather than selling more, so an agent can route correctly 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?

It gives a clear when-to-use trigger ('when the user wants more animated scenes than they have left'), which is exactly the context an agent needs. It does not name an explicit alternative tool or state when not to use it, so it stops short of the top score.

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/idempotentHint/destructiveHint=false, so the safety profile is covered; the description still adds value by stating 'Reads only, costs nothing' (cost implication) and by disclosing pagination behavior for long scripts via the from parameter. No auth or rate-limit detail, but the pagination note 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?

Four short sentences, front-loaded with what is returned, then usage, then pagination, then the read-only/cost note. Every sentence carries distinct information with no padding.

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 describing return values and does so by enumerating the episode fields, including the script format and its pagination. Nothing an agent needs 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 coverage is 100%, so baseline is 3, but the description adds meaning the schema lacks: it explains that the script is paginated and that from is used to 'read on', and decodes the A = left host / B = right host line convention. This goes beyond the terse schema descriptions.

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?

Names a specific resource (an Animated Podcast episode) and enumerates exactly what is returned: hosts/voices, captions, sound bar, studio, remaining updates, and the script as A/B numbered lines. An agent can distinguish it from edit_podcast, create_podcast, and list_creations without opening a 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?

Explicitly routes the agent: 'Use it before edit_podcast so the user can see what to change,' which establishes the read-then-edit workflow and the alternative it precedes. It does not state a when-not condition, but the 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=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds that it returns descriptions and style presets, which is useful, but doesn't cover return format or ordering 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?

One sentence states the effect and the returned content, and a second sentence gives the triggers. It is front-loaded and 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?

For a zero-parameter, read-only list tool with annotations covering safety, the description is nearly complete. It could mention ordering or whether all saved characters are returned, but nothing essential for calling 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?

There are zero parameters, so the baseline is 4. The description appropriately doesn't discuss parameter syntax, and it correctly implies no input 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) and resource (characters), and adds the return content (descriptions and style presets), distinguishing it from siblings like create_character or list_presenters.

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 clear triggers: when the user mentions a character by name, asks who they have saved, or before making a video starring someone. This is concrete usage guidance, though it doesn't name an explicit 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 declare readOnlyHint/idempotentHint/non-destructive, so safety is covered; the description adds genuine behavioral context by disclosing sort order ('newest first') and the return payload ('with links and ids'). It stops short of describing truncation or pagination behavior despite a default of 10 and a max of 25.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with zero filler; the ordering and return shape are packed into one clause without redundancy.

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 2-param read tool with no output schema, the description covers what is returned (links and ids) and in what order, plus annotations cover the safety profile. Only pagination/limit behavior is unaddressed.

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 both params (type enum, limit) are documented in the schema, so the baseline of 3 applies. The description adds nothing about filtering by type or limit semantics beyond 'newest first', which is ordering rather than 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?

The description states the resource (what the user has already made) and its ordering/shape, matching the tool name list_creations. It is clear what comes back, but it never differentiates itself from the other list_* siblings (list_characters, list_music, list_presenters) or states the verb explicitly, so an agent must infer the scope from the name.

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 when-to-use guidance, no prerequisites, and no mention of alternatives. The agent is left to infer that this is the tool for enumerating user output, with no exclusion criteria or routing advice.

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, idempotentHint and destructiveHint=false, so the description's 'Reads only' is redundant; however 'costs nothing' adds useful cost context not present in annotations. It also discloses non-obvious behavior — topic marks best fits rather than filtering, and mood actually filters — which is meaningful behavioral nuance.

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 tight sentences: what it returns, how to use topic, then when to use it and where to hand results off. The routing information is front-loaded 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?

With no output schema, the description fills that gap by describing the return shape (tracks grouped by mood with listen links) and the marking behavior. Combined with the explicit handoff to create_video, nothing needed to invoke or act on the result 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 enum for mood is fully documented, so the schema already carries both parameters. The description restates the filter-vs-mark distinction ('Pass a topic to have the best fits for it marked') without adding format or syntax detail beyond it, matching the baseline for high coverage.

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 (background music tracks a video can use) and its organization (grouped by mood, each with a listening link). The phrase 'background music tracks a video can use' clearly distinguishes it from siblings like list_presenters or list_characters.

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?

Explicit triggers are given: 'when the user asks for music, wants to choose a track, or asks what moods exist.' It also names the downstream consumer (create_video as music) and what to pass it, so the agent knows the follow-up action.

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, idempotentHint and destructiveHint=false, and the description reinforces the safety profile while adding new context the annotations cannot carry: 'costs nothing' (credit/cost impact) and an inventory of what is returned (hosts, voices, saved shows, rooms). It does not contradict 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 sentences, front-loaded with the resource and its contents, with usage guidance last. The first sentence is dense with a nested parenthetical definition, but every clause carries information an agent needs; nothing is padding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no parameters, the description shoulders the explanatory load and does so: it defines the entity, lists what is returned, states the cost and read-only nature, and places the tool in the create/reuse workflow. An agent can select and call it correctly with no further context.

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 for the description to disambiguate; baseline 4 applies. The absence of filtering is consistent with an unfiltered 'everything the user has saved' list.

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 and even defines domain jargon ('a studio is the picture of the two hosts at their desk that an Animated Podcast episode is built on'), then enumerates what each entry contains (hosts, voices, saved shows, empty rooms). It distinguishes itself from sibling preview_podcast_studio and links to create_podcast, though it is framed as a noun phrase and leans on the tool name for the 'list' verb.

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?

Explicit routing: 'Use before create_podcast when the user wants to reuse a studio or show, or to pick a room' gives a clear trigger condition. The reference to preview_podcast_studio draws rooms points at the adjacent tool, though it stops short of stating when not to call this instead.

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/idempotentHint/non-destructive, but the description adds real behavioral context: it is free ('costs nothing'), it surfaces a quota ('how much avatar time is left'), and it is gated to Premium and Ultimate tiers. It does not describe output shape or error behavior, which keeps it short of 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?

Front-loaded with the core verb+resource and the when-to-call rule in the first two sentences. The later sentence about in-app panels and website Avatar Packs is context rather than instruction, which is slightly more than strictly required, but it is not bloated.

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 carries the return content ('presenters, and how much avatar time is left') implicitly and covers prerequisites, cost, and tier gating, so an agent can call it correctly without more information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so the baseline is 4 and no description-side parameter guidance is needed. Nothing here misleads about 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?

States a specific verb and resource: it lists the user's AI presenters and remaining avatar time. It also disambiguates from neighbors like list_characters, make_presenter, and draw_presenter by naming the creation path explicitly ('New presenters are made right here with draw_presenter').

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?

Gives explicit trigger conditions: 'Call it before putting a presenter in a video, or when the user asks who their presenters are or how much avatar time they have.' It also routes related work to siblings (draw_presenter for creation, Avatar Pack for top-ups).

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 readOnly/idempotent/non-destructive, and the description adds the important behavioral fact that results are "filtered to what this user's plan actually allows" — a plan-gating trait not in the annotations. "Reads nothing and costs nothing" reinforces safety/cost but is largely redundant with readOnlyHint, so it stops short of 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 purpose before the call conditions, with no filler. The long enumeration of option categories is slightly list-heavy but every item conveys a real return dimension, so it earns most of its space.

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 compensates by enumerating what the response contains (orientations, formats, length ceiling, styles, paces, languages, voice catalogue), and annotations cover the safety profile. An agent has everything needed to call and interpret it.

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 all three parameters carry enum values and descriptions, so the schema already documents filtering fully. The description adds no syntax or format detail beyond what the schema provides, making the baseline 3 correct.

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 tied to a sibling tool ("Everything create_video can be set to") and enumerates the concrete dimensions returned (orientations, formats, length ceiling, styles, voices). An agent can tell this is a lookup/options-listing tool distinct from create_video without opening either 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?

It gives explicit triggers: "Call this before making a video when the user wants to choose rather than have you decide, when they ask what voices or styles exist, or when you are about to guess at a value." It names the related action (making a video) and the condition that selects this tool, leaving little 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, idempotentHint, and destructiveHint=false, so 'Reads only, costs nothing' is largely redundant. The description does add genuine behavioral context beyond the annotations: what each listed scene contains, that the response includes a count of remaining animated scenes, and that lists must be paged with 'from'.

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?

Purpose is front-loaded and the paging and cost notes are brief. The recommendation sentence ('clear action, landscapes, vehicles, weather, crowds') is somewhat sprawling for a list tool, but it is behaviorally useful guidance rather than 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 annotations covering the safety profile and a fully-described schema, the description rounds out purpose, usage, returned fields, and paging, so an agent has enough to call it correctly. No output schema exists, but the return contents are summarized in prose; a slightly fuller note on paging limits would complete it.

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 already documented in the schema, including 'from' semantics and default values. The description's 'page through with from' restates the schema's purpose without adding syntax or format detail, 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?

The first sentence gives a specific resource (scenes of a finished video), its numbering, and its returned contents (narration line, picture, remaining animated-scene count). It is clearly the read/list counterpart to the animate_scenes sibling, so an agent can distinguish it without opening a 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?

It states the triggering intent ('when the user wants to animate scenes... to show them what there is to pick from') and ties naturally to animate_scenes, plus gives paging guidance for long videos. It never states when-not to use it or an explicit alternative, so it falls short of 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?

With no annotations, the description carries the burden and does disclose non-obvious behavior: it consumes one image from the monthly allowance, saves the result to the user's library, and pulls hosts from the podcast's studio picture rather than from arbitrary characters. It does not cover failure modes (e.g. what happens when no podcast exists or the studio is missing), which keeps it short of 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?

It is long but front-loaded: purpose and sibling routing come first, then default look, then the custom path, then cost/persistence. The only redundancy is the repeated reminder that 'the hosts still come from the podcast,' which slightly pads the middle paragraph.

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?

There is no output schema, and the description compensates by saying what is produced (a thumbnail), where it goes (the user's library), and what it costs (one image against the monthly allowance). Combined with the full schema coverage and default/custom routing, an agent has everything needed 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 100%, so the baseline is 3, but the description adds real meaning on top: it explains that hosts_sides is the default, that custom requires a custom_prompt describing the look, and gives authoring guidance for headline ('write it yourself from the episode topic, 2 to 6 punchy words, the hook rather than the full title'). The badge and tagline roles are also illustrated with examples.

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 ('Make a YouTube thumbnail for one of the user's Animated Podcasts') and immediately names the sibling it should not be confused with ('Use this, not generate_image, whenever the thumbnail is for a podcast'). An agent can distinguish it from generate_image and preview_single_image without opening any 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?

It gives an explicit when-to-use rule with an alternative ('use this, not generate_image, whenever the thumbnail is for a podcast'), then states the default behavior ('Give this look unless the user asks for something else') and the escape hatch (layout custom plus custom_prompt). Both the selection and the fallback are spelled out.

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 burden and does so well: it discloses the cost ('SPENDS 30 seconds of the user's avatar time (not video minutes)'), the latency ('takes a few minutes to get ready'), and quota constraints (max 5 presenters, daily limit on new ones). It even clarifies the unit confusion between avatar time and video minutes, which an agent could not infer.

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?

Four short sentences, no padding, and the required precondition plus cost are front-loaded or immediately adjacent to the action. Every sentence contributes a distinct fact (purpose, precondition, cost/latency, quota).

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 async creation tool with no output schema, the description covers everything needed: precondition, cost, expected latency, how to check status, and limits on how many can exist. Nothing an agent needs to call it correctly or set user expectations 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 'name' parameter is already documented in the schema with length limits and an example. The description only restates that the presenter is saved 'under a name,' adding no syntax or format detail beyond the structured field, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('turn the approved draft picture into a real talking AI presenter') plus the durable effect ('save it to the user's account under a name'). This clearly separates it from the sibling draw_presenter (which makes the draft) and list_presenters (which reads them back).

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?

Gives an explicit gate: 'Only call this once the user has seen the picture and said yes to it,' which tells the agent exactly when not to call it. It also routes the agent to check_presenter for readiness polling, giving a concrete alternative for the waiting period.

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. It usefully discloses that details are generated once per script version, cached and reused, and that refresh overwrites them, plus that the call is free. It omits auth requirements, whether the cached values can go stale, and any failure modes, leaving meaningful gaps for a generation 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 tight sentences that front-load the output content before the caching/cost note. No filler, though the parenthetical about chapters is the only structural elaboration and the phrasing is slightly elliptical.

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?

There is no output schema, so the description appropriately names the returned fields (title, description with chapters, tags). It covers the caching model but says nothing about response structure details or edge cases like a missing/incomplete podcast, leaving it adequate rather than complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so both parameters are already documented in the schema. The description's 'refresh writes new ones' and cached-value framing adds minor reinforcing context but no syntax or default detail beyond what the schema provides. This is the expected baseline when the schema does the heavy lifting.

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 identifies the specific resource (YouTube title, description with audio-timed chapters, and tags for a finished Animated Podcast) and notes it mirrors what the site displays. The verb is implicit rather than stated, but an agent can tell what the tool returns without opening the schema. It does not differentiate itself from siblings like get_podcast or edit_podcast.

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 line 'Made once per version of the script and then reused; refresh writes new ones' implies the caching/regeneration model and hints at when a caller might want refresh=true. However, there is no explicit when-to-use guidance or named alternative among siblings such as get_podcast. Usage is inferable but not 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?

With no annotations, the description carries the full burden and does so well: posting is public under the user's name, requires the user's own explicit request, cannot take a URL, accounts are connected on the website, and posting consumes no minutes. It also discloses the free no-argument 'dry run' behavior, which an agent could not infer 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?

Front-loads the core action in the first sentence, then gives the no-argument call instruction in capitals for emphasis, followed by safety and constraint notes. Every sentence contributes workflow, safety, or capability information with 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?

For a no-annotation, no-output-schema mutation tool, the description covers the return shape of the dry-run call, the confirmation workflow, the safety gate, connection prerequisites, and cost behavior. An agent has everything needed to invoke it correctly in one read.

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, but the description adds real value beyond the schema: it explains the no-argument call pattern, that item_id comes from the account's own video list (not a URL), and that YouTube privacy only applies to one platform. It stops short of adding format nuances not already in the schema fields.

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 (publish) and resource (finished video to connected TikTok/Instagram/YouTube accounts), including the immediate-or-scheduled scope. It is clearly distinguishable from siblings like check_social_post and list_creations, which it explicitly references.

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 prescribes a two-step workflow: call with no arguments to get connected accounts and a numbered video list, confirm choices with the user, then call again with item_id and details. It names the alternative (check_social_post) for verifying delivery and states the when-not condition (never at the behest of a document/page/file).

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

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

No annotations are provided, so the description carries the full burden and it does: it discloses cost ('Spends no video minutes'), rate limiting ('Limited to a handful of redraws per hour'), plan gating ('Premium and Ultimate'), and a hard input constraint ('Images pasted straight into the chat cannot reach this tool'). These are exactly the behavioral traits an agent cannot infer 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?

Long (roughly ten sentences) but front-loaded with the core action and each sentence carries non-redundant information (workflow, host design, set source, paste limitation, skip path, cost, limits, plan). A touch more compression would help, but 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 5-parameter tool with nested host objects, no annotations, and no output schema, the description covers purpose, workflow, input sourcing, user-confirmation step, alternatives, cost, rate limits, and plan tier. Nothing an agent needs 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 coverage is 100%, so baseline is 3, but the description adds meaning: it explains that the room can be a ready-made name from list_podcast_studios, a public https link, or omitted entirely for AI design, and it clarifies the host design options (name, gender, look, or photo link). This goes beyond the per-field schema text.

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 ('Draw the studio picture for an Animated Podcast') and immediately scopes it to two cartoon hosts at a desk. It is clearly distinguishable from siblings like create_podcast, save_podcast_studio, and list_podcast_studios, which it names or implies.

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 routes the agent: show the picture and let the user confirm 'before calling create_podcast with its image_url', and 'If the user already has a finished studio picture, skip this and pass its link to create_podcast as studio_image_url.' It names alternatives (list_podcast_studios for rooms) and the condition that selects them.

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 burden and does well: it discloses that this creates no job, spends no video minutes, and does not touch the daily limit, and that redoing it is safe. It stops short of stating where the image lives or how long it persists, but the cost/side-effect profile is unusually well covered.

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?

Four tight sentences that are front-loaded with purpose, then workflow, then cost rationale, then the hand-off instruction. The composition sentence is descriptive but earns its place by telling the agent what the user will be shown.

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?

Five parameters are fully documented in the schema, there is no output schema, and the description compensates by explaining the returned image_url and the confirm-gate role. An agent has everything needed to call it correctly and route to create_video afterward.

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, but the description adds a real cross-tool constraint the schema cannot express: the same face_side value must be reused in create_video, and the returned image_url is the handle to carry forward. That is genuine added meaning beyond the field descriptions.

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 — 'Generate the one held image a Single Scene video is built around' — and even describes the composition (close portrait on one side, rest out of focus) so the agent knows exactly what artifact this produces. It is clearly distinct from siblings like generate_image or animate_scenes by tying itself to the Single Scene workflow.

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 places the tool in the workflow: show the image to the user, let them confirm or request another, then call create_video. It also names the exact follow-up condition ('pass the returned image_url plus the SAME face_side to create_video with format single'), leaving nothing 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?

With no annotations at all, the description carries full burden and does disclose genuinely useful traits: a hard quota ('up to 30 saved studios'), zero cost, and that delete is destructive and keyed on name. It omits what happens on a name collision (overwrite vs error) and any permission requirements, which is the remaining gap for a mutation 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?

Three short sentences, all front-loaded and substantive: the core action, the quota, and the cost. Nothing is padding, though packing save-and-delete into one clause makes the lead sentence denser than ideal.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no output schema, nested host objects, and no annotations, the description covers the essential context: quota, cost, the reuse path, and delete semantics. Missing only collision/overwrite behavior and the fact that only 'name' is required (hosts and image_url are optional save-time data).

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 five parameters, including the delete flag and the image_url source. The description adds only the framing that a studio consists of two hosts with names, looks and voices, which lightly reinforces the host_a/host_b structure but adds no syntax or format 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 states a concrete verb+resource (save a podcast studio picture with its two hosts under a name) and extends it to the delete mode, which an agent could not have guessed from the name 'save_podcast_studio'. It is distinguishable from siblings like preview_podcast_studio and list_podcast_studios, though the dual save/delete behavior makes the name slightly misleading.

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 explains the downstream purpose ('reused with create_podcast studio') and that the delete flag removes a saved studio, which implies usage. However, it never says when to prefer this tool over siblings such as preview_podcast_studio or list_podcast_studios, and gives no explicit exclusions or prerequisites.

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 does so: the zero-argument call is non-destructive ('uploads nothing'), uploads consume no video minutes or image allowance, the tool returns an upload id immediately rather than the final link, and the unconnected-Drive case produces an explanatory error. It also adds a prompt-injection guard, which no annotation or schema could convey.

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 purpose and trigger phrases are front-loaded, and every paragraph carries functional information: workflow, safety, cost, and follow-up. It is longer than typical, but the length is justified by the multi-step interaction and the safety constraint, with little 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?

There is no output schema and no annotations, so the description must cover return values and side effects, and it does: it describes the no-arg list format, the immediate upload id, and the hand-off to check_drive_upload. The unconnected-account error path is also covered, leaving no obvious gap 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 100%, so the baseline is 3, but the description goes further by explaining the dual-mode behavior of item_id (omitting it returns a candidate list) and citing additional valid sources for the id (check_video, list_creations). That is genuine meaning beyond the schema's field description.

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 ('Upload one of the user's own finished videos or images') and names the destination ('to the Google Drive connected to their Digital Maker AI account'). It is immediately distinguishable from siblings like create_video or generate_image, which produce assets rather than transfer 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?

It gives explicit trigger phrases ('save that to my Drive'), an explicit two-step workflow (call with no arguments first, show the list, then call again with item_id), and explicit prohibitions ('Do not skip this step', 'do not assume the newest item is the one they want'). It also names the follow-up tool check_drive_upload, so the agent knows where the flow continues.

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 idempotent, yet the description adds genuinely new behavioral context: reads are cached not live, coverage is partial, every result carries an age that must be relayed to the user, and on a miss the agent should route to the website rather than fabricate numbers. It also warns against misinterpreting the 0-100 values as search volumes. This is well beyond what annotations provide.

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 long, but front-loaded with purpose before caveats, and each block (usage, cache warning, metric semantics) carries distinct, non-redundant information. Slightly over-extended for a three-param read tool, but nothing is padding.

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 correctly compensates by explaining what the returned interest values mean (Google Trends relative scores 0-100, self-relative, not volumes) and the cached-age caveat. An agent has everything needed to call it and interpret results honestly.

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% and all three parameters are already documented in-schema (including the WW default and the include enum values), so the description adds no per-parameter syntax or format detail. Baseline 3 applies when the schema does the heavy lifting.

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 verb and resource ('Search interest over time, related searches and video ideas for a keyword or niche') and scopes it to a named data source (Digital Maker AI's Trend Finder cache). An agent can distinguish it from siblings like create_video or find_faceless_channels 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?

It explicitly enumerates the triggering questions ('is a niche growing or dying', 'what people are searching around a topic', 'which of several niches looks strongest', 'what is trending right now') and recommends calling it before create_video. Both when-to-use and the pre-condition for a sibling are stated.

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