Search attributed motion recipes and prepare prompts for your coding agent. No API key required.
Scored across 3 tools
The three tools form a clean pipeline: search_motion finds IDs, get_motion reads a recipe's details, and prepare_motion assembles a prompt. Their descriptions explicitly cross-reference each other, so boundaries are unambiguous.
All three follow a strict verb_noun snake_case pattern (get_motion, prepare_motion, search_motion) on a single shared noun, making the pattern fully predictable.
Three tools is on the thin side but each earns its place in the discovery-to-preparation workflow. A lightweight library surface doesn't need more than this.
Discovery, retrieval, and prompt preparation are all covered, with license/source handled inside get_motion. Minor gaps exist: no way to enumerate categories, stacks, or the full catalog without searching, and no listing of available recipe IDs.
Read a recipe prompt, dependencies, customization parameters, verification, source, and license. Source-only references return a link and no prompt.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish that this is a safe, read-only, idempotent operation. The description adds useful behavioral detail by explaining that source-only references return a link and no prompt, which is not covered by 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?
The description is two tight sentences with no wasted wording. The main retrieval behavior is front-loaded, followed by the important source-only caveat.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers returned fields and the source-only edge case, which helps given there is no output schema. However, it leaves the id parameter semantics unexplained and does not route the agent among sibling tools, leaving clear gaps for a get-by-id operation.
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 schema has 0% description coverage for the single required id parameter, so the description should compensate but does not. It never explains what the id identifies, its format, or how it relates to the recipe prompt being read.
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 read operation and enumerates the returned content: recipe prompt, dependencies, customization parameters, verification, source, and license. It is clear what the tool retrieves, though it does not explicitly differentiate itself from sibling tools such as search_motion or prepare_motion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no explicit guidance on when to use this tool versus alternatives. The note about source-only references describes return behavior, not usage conditions or exclusions, and the sibling tools are not mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Prepare a motion prompt for the user coding agent using a recipe ID and project brief. Deterministic text preparation; does not render, call an LLM, or spend money.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| brief | Yes | ||
| customization | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover readOnly/idempotent/non-destructive/closed-world, and the description adds genuinely new behavior facts: deterministic, no LLM call, no cost. It stops short of describing the output shape (a prompt string? object?), which would have made it 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 tight sentences with the purpose front-loaded and the behavioral caveat immediately after. 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?
No output schema exists, yet the description never says what the prepared prompt looks like or how it is returned. Combined with the undescribed nested customization object, an agent can call the tool but cannot make full use of its inputs or predict its result.
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?
With 0% schema description coverage, the description must carry parameter meaning, and it only covers two of three: 'recipe ID' for id and 'project brief' for brief. The nested customization object (accent, duration, aspectRatio) is never mentioned, leaving its most complex parameter 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?
States a specific verb and resource: 'Prepare a motion prompt' from a recipe ID and project brief. That distinguishes it from the lookup-oriented siblings search_motion and get_motion, though the contrast is implied rather than stated.
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 clarifies the tool is a deterministic text-preparation step that does not render or spend money, which edges toward usage guidance. But there is no explicit 'use this before X' framing or named alternative, so the when-to-use case is only inferable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find motion recipes by effect, use case, category, or stack. Returns compact matches; select an ID and call prepare_motion. No generation or network fetches.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | No | ||
| stack | No | ||
| category | No | ||
| verifiedOnly | No | ||
| includeReferences | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive, closed-world; the description reinforces this with "No generation or network fetches" and adds return-shape context ("compact matches"). That is useful but partially duplicative of openWorldHint=false, and pagination/limit behavior is not described.
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 with zero padding, front-loading the search purpose before the next-step guidance and the negative constraint.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a six-parameter tool with no output schema and no per-parameter documentation, the description should explain the boolean filters and result-count behavior. It covers purpose, next step, and return brevity adequately, but leaves callers guessing about verifiedOnly, includeReferences, and limit defaults.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the six parameters carry no inline semantics. The description implies the roles of query, category, and stack but says nothing about limit, verifiedOnly, or includeReferences, leaving half the surface 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?
States a specific verb+resource ("Find motion recipes") and enumerates the retrieval dimensions (effect, use case, category, stack). It names the downstream sibling prepare_motion, though it never distinguishes itself from get_motion, which is the other adjacent read tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Gives a clear follow-on path: "select an ID and call prepare_motion," which tells the agent this is a search-then-fetch flow. It stops short of explicit when-not conditions or a direct comparison against get_motion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
get_motionprepare_motionsearch_motionSearch GitHub, npm, PyPI, StackOverflow, ArXiv from one MCP — built for coding agents.
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