nanoodle

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

With no annotations, the description carries the full burden and largely delivers: it discloses the payment model ($0.06 deposit in Nano, settles at actual cost +20%, change returned, no account needed), observed runtime (~86s), last-run cost, and that outputs are saved to disk with file paths in the result. It also clarifies that the rig/animation baking happens locally outside this tool. It stops short of error/retry behavior, keeping it below 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?

The text is a run-on blend of pipeline notation, payment mechanics, a pasted example URL, and marketing copy, with no clear front-loading of the core action. Several fragments ('id…', 'resize:Reference') are truncated and unreadable, costing more than they add. It is not under-specified, but it is poorly structured.

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 must explain returns — and it does ('returns image + image … saved to disk (file paths in result)'), along with cost, timing, and the division of labor with the local rig.mjs skill. For a complex multi-step paid pipeline this is fairly complete, missing only failure/retry handling.

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 three parameters, including the nuanced _payment_id flow — baseline is 3. The description adds only a loose mapping of 'text:Character' and 'llm:Character designer' into the pipeline, which is marginal beyond what the schema provides.

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

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

The description eventually states that the graph 'generates a character reference and parts sheet' and that a local skill bakes animated sprites, which is a concrete verb+resource. However, the leading sentence is a fragment ('Description → canonical character → separate cutout parts → local rig and animation baking') and the pipeline arrow dump makes the actual purpose hard to extract at a glance. It is distinguishable from siblings like mix-the-references, but only after parsing a lot of noise.

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 provides an example use case (a playable fighting game, with a link) but never states when to choose this tool over siblings such as image-model-arena or mix-the-references, nor any prerequisites. The 'One text call + two image calls' line implies a multi-step flow but does not tell the agent when or how to invoke it versus alternatives. Guidance is contextual rather than directive.

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 disclose genuinely non-obvious traits: per-request deposit quoted from length and maxChars, settlement at the lesser of metered cost plus 20% and the deposit with refund of the difference, operator absorbing overruns, the fallback being charged twice, direct-wallet spending being uncapped, and a concrete cost/latency datapoint (~$0.0033, ~57s). The gap is that the rewrite behavior itself (what a rejected rewrite means, retry semantics) is only hinted at rather than stated, and the pipeline dump crowds out more useful disclosure.

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 opening is a raw execution graph ('text:Draft -> text:Context -> join:Judge: +A ...') that reads as machine-generated trace output rather than a front-loaded summary. The most decision-relevant facts — what the tool does and its cost model — are buried after or inside that noise. Substantial trimming would make it far more usable.

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 eight-parameter, no-output-schema tool with a paid two-phase flow, the description covers cost, latency, and the payment-id phase transition, which is the hard part. What it omits is the selection context (versus subnano_comment) and a clear statement of the rewrite contract, leaving the agent to infer purpose from the pipeline listing.

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

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

Schema coverage is 100%, so the schema already documents all eight parameters thoroughly, including keep, ending, title, and maxChars semantics. The description adds only that request length and maxChars drive the deposit quote. Baseline 3 is appropriate; the description does not need to, and largely does not, expand 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 implies a rewrite/humanize operation through the five named writer voices and the 'voice: friend/forum/peer/plain/quick' labels, and it does say 'returns text'. But the actual verb+resource statement is never made plainly — the reader has to reconstruct 'rewrites a draft comment' from an internal pipeline graph. It also never distinguishes itself from the sibling subnano_comment, so selection between the two is guesswork.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no statement of when to choose this tool over subnano_comment or any other sibling. The only conditional guidance present is operational: the _payment_id phase-3 flow and the note that the Starry Ocean fallback double-charges. Useful, but it does not answer 'when should an agent call this'.

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 well: it discloses the pipeline stages, that the result is a GLB saved to disk with the file path in the result, the $0.51 Nano deposit and settlement at cost+20%, that no account is needed, and quality caveats about thin parts going soft at some angles. This is substantial behavioral context beyond 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?

The purpose is front-loaded, but the description is cluttered with a truncated pipeline fragment, redundant pipeline arrows, a long example, and trailing quality caveats. Much of it is useful, but the structure is uneven and not tightly concise.

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 5-parameter tool with no output schema and no annotations, the description covers the pipeline, the returned GLB/file path, payment mechanics, and quality limitations. It is largely complete, with only minor gaps around exact parameter roles that the schema itself covers.

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 even without parameter detail in the description. The description does add some conceptual meaning (Object_idea as plain words, Concept_description as LLM-written concept text, render rules as fixed join input), but it does not explain _payment_id or TRELLIS.2_GLB in a way that meaningfully exceeds 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?

The description clearly states a specific pipeline: an object idea becomes concept art and then a 3D GLB model, with named stages (LLM concept description, join render rules, TRELLIS.2). This is a clear verb+resource purpose. It does not explicitly contrast itself with sibling tools like character-sprites or tiny-world-film, so it falls short of the sibling-differentiation required for a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives practical input guidance ("Write one object in plain words") and an example, plus cost/payment context. However, it never states when to choose this tool over alternatives or when not to use it, leaving usage selection implied rather than explicit.

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 entire burden and does so well: it discloses cost structure ($0.40 deposit, settles at actual model cost + 20%, last run $0.264), payment currency and that no account is needed, expected latency (~73s), that four paid images are generated, and that outputs are saved to disk with file paths in the result.

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 cost details are front-loaded, but the middle is cluttered with a concatenated model/output chain and a truncated fragment ('test your o…'), which is a structural defect. 'Compare all outputs before choosing' largely restates the opening sentence.

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 specifying each model, aspect ratio, resolution, and that files are written to disk. Pricing, latency, and payment handling are covered, leaving little an agent would need before calling 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 coverage is 100%, so the two parameters are already documented, including the _payment_id phase-3 flow. The description only lightly reinforces the shared brief ('Change the shared brief to test your...'), which is the baseline expectation 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 opening sentence names a specific verb and resource ('Compare four image models on the same brief') and the run/return summary enumerates exactly which four models are invoked. An agent can distinguish this from siblings like storyboard-relay or character-sprites 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 clear operating context: change the shared brief to test variants, and 'Compare all outputs before choosing' tells the agent what to do with the result. It stops short of naming when NOT to use it or an explicit alternative tool, so it is not a full 5.

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

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

With no annotations, the description carries the full burden and does real work: it discloses the model (flux-3/klein/edit-image), output resolution (1024×1024), on-disk persistence, the NanoGPT payment model ($0.08 deposit, Nano XNO, actual cost + 20%, change returned, no account needed), and even known failure modes of a sample run. The only gap is the multi-phase payment/watch flow, which lives in the schema rather than the description.

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 is front-loaded, but the middle section is a garbled concatenation of node/port arrows ('text:Who · character -> text:What they wear · outfit -> ...') that reads as raw wiring rather than prose, and the example plus failure-mode note make it longer than needed.

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 six-parameter, zero-required generation tool with no output schema, the description supplies the essentials: what the output is (image saved to disk, path in result), the cost/settlement model, and the port-order contract. The phased payment handshake is deferred to the schema, which is acceptable given the _payment_id parameter documents 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 coverage is 100%, so the baseline is 3, but the description adds real meaning: it maps each node to a port-ordered input (image 1 = character, 2 = outfit, 3 = prop, 4 = place) and explains that the mix instruction names each image by number. That ordering/naming semantic is not fully evident from the schema field names alone.

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 opening sentence states a specific verb and resource ('Mix four references into one picture') and the description names the underlying model (FLUX.3 Klein Edit). It clearly explains the four-node structure (who/wear/hold/where), so the operation is unambiguous, though it never names a sibling like image-model-arena or character-sprites to differentiate itself.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage context is implied through a worked example ('a paper-craft fox ... wears a yellow knitted raincoat ...') and the port-order requirement, but there is no explicit when-to-use vs. when-not guidance and no routing to alternative image tools in the sibling set.

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 actually delivers: it discloses the payment model ($0.63 deposit per call in Nano/XNO, settles at actual model cost +20% with change returned, no account needed), cost and latency evidence (last run $0.2601, ~47s, ~$0.27 sampled), the output destination (video saved to disk, file path in result), and non-determinism tied to the seed. The payment-phase mechanics are the main gap, though the schema's _payment_id description partly covers 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?

The pipeline is front-loaded, which is good, but the text then repeats the same flow in a second notation and cuts off mid-word ('Hold both f…'), leaving genuine clutter. Cost and latency figures are useful, but the duplication and truncation cost the definition readability.

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 multi-stage, paid, no-annotation tool with no output schema, the description covers the pipeline, output artifact and its location, cost, latency, and payment settlement — enough for an agent to invoke it knowingly. Remaining gaps are the exact result shape and failure behavior, which are minor given the schema's _payment_id documentation.

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

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

Schema description coverage is 100%, so the schema already documents all four parameters including defaults and the _payment_id protocol; baseline 3 applies. The description only gestures at the seed and the two LLM-written prompt parameters, adding no syntax or format 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?

The description lays out a concrete pipeline (seed → LLM still prompt → text-to-image → LLM motion prompt → MiniMax H3 Max i2v at 480p/5s) and the output is stated as a fight/clash video saved to disk. That is specific, but it is presented as a duplicated, partially truncated flow chart ('Hold both f…', then the same steps again in 'text:Fight seed -> llm:...' notation), so the agent must assemble the actual purpose from fragments. No sibling tool is named or contrasted.

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 only usage cue is 'Change the seed; both LLMs rewrite prompts,' which describes a side effect, not when to choose this tool over siblings like photo-to-video, storyboard-relay, or talking-avatar. There is no when-not-to-use, no prerequisite statement, and no alternative named.

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 zero annotations the description carries the full burden and does so: it discloses the payment model (0.51 deposit in Nano, settles at model cost +20%, change returned, no account), expected latency (~1m30s), a real prior run cost, output persisted to disk with a file path in the result, and an honesty caveat that the example does not prove a seamless loop.

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 workflow and cost facts are front-loaded, but the body is a run-on with a truncated fragment ('a s…') and a paragraph of example/thumbnail caveats. Most sentences earn their place, yet the density could be tightened considerably.

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 exists, and the description correctly explains that a video is written to disk with the path in the result. Cost, latency, and the iterative loop are covered, and the _payment_id phase sequencing is handled in the schema. What is missing is any statement of failure modes or what happens on a rejected/refunded payment.

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; the schema already documents all three parameters including defaults and the _payment_id sequencing rules. The description adds modest value by tying Still_brief to the first frame and Motion_brief to the animation and by advising they be changed together, but nothing about formats or constraints 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?

The description names a concrete two-stage pipeline: a first frame from Muse, then a 5-second 480p animation from MiniMax H3 Spicy. That is a specific verb+resource an agent can act on. It does not explicitly contrast itself with siblings like storyboard-relay or tiny-world-film, so it stops short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives iteration guidance ('Change the still and motion briefs together', 'Inspect object stability and motion'), which implies re-running with revised briefs. However, there is no statement of when to choose this over a sibling tool or what inputs make it unsuitable; usage must be inferred.

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 discloses the mandatory nature ('no switch to skip'), the ffmpeg dependency, the per-picture decision model, and a detailed payment lifecycle (quoted deposit, cap, metered cost +20% settlement, skipped pictures on budget exhaustion, refund of difference). That is far more than a restatement of the name.

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 and decision rule, then progressively adds modality requirements and billing. The final payment sentence is dense and long, but every clause carries operational information an agent needs; nothing 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?

No output schema exists, and the description compensates by describing the decision shape (one yes/no per picture, overall pass only if all yes). Cost behavior and failure modes are covered. Minor gap: the exact response structure for a failed/partial result and what the payment-required phase returns is left to the caller's inference.

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

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

Schema description coverage is 100%, so every parameter including the payment id is already documented. The description adds the useful semantic that the frames parameter determines how many independent decisions are made and that all must pass, which is a small but real gain over 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 ('say whether ... is safe for work') and resource (image or evenly sampled video frames), plus the aggregation rule ('passes only when every picture is yes'). No sibling tool overlaps with content moderation, so the agent can route unambiguously.

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?

Clearly separates the two modes (still image vs video) and gives the operational precondition that video sampling requires ffmpeg while a still does not. It also warns there is no switch to skip the check. No alternatives need naming since no sibling performs this function, so exclusion guidance is not applicable.

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 return artifact (audio, music-3, file path in the result), the payment mechanics ($0.37 deposit per call in Nano/XNO, settles at actual cost + 20%, change returned, no account needed), realistic latency (~2m 35s), last-run cost, and a candid caveat that output is model-assisted review rather than human listening sign-off.

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 is front-loaded, but the body then mixes pipeline notation, truncated text ('one musical di…'), payment terms, a gallery link, and a review disclaimer in one dense block. Much of the payment/example detail could be split out, and the truncated fragment is wasted 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?

There is no output schema, so the description correctly explains the return (audio file saved to disk, path in result), and it covers cost, latency and the no-account payment model. The multi-phase payment/watch mechanics are only partially explained here, though the _payment_id schema description compensates, keeping this just short of complete.

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

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

Schema description coverage is 100% and every parameter is optional with a documented default, so the schema already does the heavy lifting and baseline 3 applies. The description does add a little meaning by mapping the three system-prompt parameters onto ordered pipeline stages (write lyrics, arrange, identify clashes), but it adds no formats or constraints beyond what the schema states.

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 outcome: it writes lyrics, arranges a song, builds an avoid-list, then runs a music model to produce audio (music-3) saved to disk. This clearly distinguishes it from siblings like character-sprites, photo-to-video and talking-avatar. It loses a point because the purpose is buried in pipeline arrows and payment logistics rather than stated upfront in a single crisp sentence.

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 tells the agent what inputs to start from (a song idea plus preferred bands) and implies this is the audio-generation path, but it never says when to choose this over alternatives or when not to use it. There are no prerequisites stated beyond the payment note, so usage is only implied.

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

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

With no annotations, the description carries the full burden and does well: it discloses the output shape (image + image + image + text), that media is saved to disk with file paths in the result, the pricing model ($0.19 deposit in Nano/XNO, settled at cost + 20% with change returned), that no account is needed, and measured latency/cost (~2m 4s, last run $0.0652). It omits the payment/watch lifecycle, but that gap is covered by the _payment_id schema description.

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 the closing cost/latency sentences are front-loaded and earn their place, but the middle pipeline chain is a long, dense arrow/semicolon run that is hard to parse, and the prose summary of it trails off with an ellipsis ('Muse Edit uses the draft…'). It is information-rich but structurally uneven.

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 high-complexity 6-parameter, multi-model, pay-per-call tool with no output schema and no annotations, the description covers output composition, storage, cost, latency, and the internal pipeline. The remaining gaps are selection guidance relative to siblings and the asynchronous payment/watch flow, the latter partially covered by the schema.

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

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

Schema description coverage is 100%, so the baseline would be 3, but the description's pipeline notation maps each parameter name to its position and role in the chain (First scene → Muse opens the story, Next beat → stages the next beat, Continuity rules → join, Continuity notes and Draft continuity check → llm checks), adding ordering and dependency semantics the schema doesn't convey.

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 opening sentence gives a specific verb+resource ('Make a two-frame story through a handoff between models') and names every participating model and its role, so the agent understands this is a multi-model two-frame pipeline rather than a single-image generator. It does not, however, explicitly contrast itself with siblings like tiny-world-film or photo-to-video, which would be needed for a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use or when-not-to-use guidance and no mention of the sibling tools that also produce narrative imagery (tiny-world-film, character-sprites). The description explains how the pipeline works internally but never tells the agent which situation should select this tool over its alternatives.

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

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

With no annotations, the description carries the full burden and does so well: it discloses stage order, that a 'no' gate does not discard the comment, the quote-as-cap pricing with 20% settlement and refund of the difference, and that the run halts before any stage the remaining budget cannot cover. What is missing is failure behavior of individual stages (timeouts, model errors) and confirmation of whether anything is posted publicly, which is notable given the 'safe to post' framing.

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 pipeline order is front-loaded in the first sentence, which is the right structure for a multi-stage tool. The final pricing sentence is a dense run-on that packs quote, settlement rate, budget cutoff, and refund into one breath, costing some readability but carrying information the absent annotations would otherwise have to supply.

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 4-param tool with no output schema, the description names all four return values (comment, gate verdict, funny score, per-step cost) and states the mounting prerequisite, which covers what an agent needs to interpret results. Stage-level error handling is the remaining gap but is secondary to correct invocation.

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

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

Schema description coverage is 100%, so the baseline is 3. The description mentions that the quote derives from 'its context and guidance' but adds no syntax, format, or interaction detail beyond what the schema already documents for Draft, Context, Guidance, and _payment_id.

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 concrete pipeline (refine, humanize-comment, d1 gate, d1 funny score), the models used, and the outputs, so the agent knows exactly what the tool produces. It references the humanize-comment sibling as an internal stage but never states when to pick this tool over that standalone sibling, which is the only thing keeping it from a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied by the pipeline narrative; there is no explicit 'use this when...' framing or exclusion versus the standalone humanize-comment tool. The one hard rule given is a prerequisite: the humanize-comment graph must be mounted, which is genuinely useful but is setup rather than selection 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, the description carries the full burden and does so well: it discloses the multi-model pipeline, the return format (video 'longcat-avatar-1.5' saved to disk with a file path in result), the payment model ($0.66 Nano deposit settling at actual cost +20% with change returned), the no-account requirement, and that generation can take several minutes. The only gaps are interaction specifics like streaming/monitoring behavior and failure handling.

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 is front-loaded, but the body is dense and unevenly ordered, mixing pipeline mechanics, pricing, and caveats, with a visibly truncated fragment ('Keep…'). Several sentences carry useful detail, yet the pricing and example notes 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 paid, multi-stage generation tool with no annotations and no output schema, the description is largely complete: it covers what is produced, where it is saved, cost/payment mechanics, expected latency, and a sample link. Minor omissions (concurrency, retries, resolution/frame caveats beyond '480p') remain.

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

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

Schema description coverage is 100%, so the schema already documents all four parameters, including _payment_id's two-phase flow. The description adds only marginal value via the input-flow line ('text:Presenter look -> text:Spoken script -> text:Delivery -> ...'), which hints at stage ordering but no new syntax or semantics 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?

The description states a specific verb and resource: 'Create a short spoken introduction with a fictional presenter,' and details the pipeline (Muse portrait, MiniMax Speech, LongCat combine) and the artifact returned. It is clearly a talking-avatar video generator, distinct from siblings like sing or photo-to-video, though it never explicitly names or contrasts those siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is an example use case (a fictional presenter reading a short workshop introduction) but no explicit guidance on when to choose this tool over alternatives such as photo-to-video or sing, nor any stated exclusions. Usage must be inferred from the example and pipeline description.

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

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

With no annotations provided, the description carries the full behavioral burden and does so well: it discloses the payment model ($0.65 deposit in Nano/XNO, settles at actual model cost + 20%, change returned, no account needed), an observed runtime (~2m 11s, last run $0.27), and that outputs are image + video saved to disk with file paths in the result. It stops short of describing failure modes or what happens if payment is abandoned.

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 pipeline and output information is front-loaded and dense, with each clause carrying real content (stages, outputs, pricing, runtime). It is slightly disorganized and contains a visibly truncated fragment ('Defaul…'), which costs a point.

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

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

For a zero-required-param, three-stage generative pipeline with no output schema and no annotations, the description supplies the essential return shape (image + video with file paths) plus pricing and runtime expectations. The two-phase _payment_id handshake is covered by the schema rather than the description, leaving only minor gaps.

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 World, Motion, and _payment_id are already fully documented, including the two-phase payment flow for _payment_id. The description's flow diagram ('text:World -> text:Motion') adds marginal pipeline-mapping meaning but no syntax or format detail beyond the schema, so the baseline 3 applies.

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

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

The description conveys that this tool generates a short film by chaining three models (Muse builds a miniature, Wan animates it, Mirelo adds foley), and the pipeline notation clarifies the stages. However, it is narrated through model names rather than stated as a crisp verb+resource, and it never distinguishes itself from siblings like photo-to-video or storyboard-relay.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use guidance and no named alternatives among the sibling tools. The line 'Compare the silent and sounded takes' hints at a comparison use case but does not tell the agent when to pick this over photo-to-video or storyboard-relay.

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