partreel

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

With no annotations, the description carries the full burden. It reveals what the response includes (parameters, verification status, datasheet, absolute URLs for four formats), which is helpful. It omits any error behavior (e.g., unknown id) or side effects, but for a read-only getter this is a reasonable 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 description is one focused sentence, front-loaded with the core action, and every clause adds concrete value. No filler or 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?

Given this is a simple single-parameter read operation with no output schema, the description adequately covers what the user gets. It is complete enough for an agent to invoke with an id and understand the returned detail set.

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 input schema already fully documents the single parameter with an example, so schema coverage is 100%. The description only reinforces 'by id' without adding extra meaning, meeting the baseline for schema-driven clarity.

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 verb and resource: 'Get full detail for one part by id' and enumerates the exact content returned (parameters, verification status, datasheet, download URLs). This distinguishes get_part from siblings like list_parts and search_parts, which cover broader or different use cases.

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

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

The phrase 'by id' implies usage when you already know a specific part identifier, and the content list suggests a full-detail retrieval. However, it does not explicitly contrast with sibling tools (e.g., 'use search_parts to find an id') or state when not to use it, so guidance remains implicit 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 provided, the description carries the full burden of behavioral disclosure. It clarifies that the tool only provides instructions ('Get machine-readable instructions'), implying a read-only operation, and does not actually add a part. This makes the tool's behavior transparent, though it could more explicitly state its read-only nature.

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 entire description is a single sentence that front-loads the main action and purpose. It packs the content areas and usage cue into minimal words without unnecessary elaboration.

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, no-output-schema tool, the description is complete: it states the purpose, content, and when to use it. It is simple enough that nothing major is missing.

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

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

The tool has zero parameters, so there is no parameter semantics to explain. The description does not need to add parameter-level detail, and the baseline of 4 is appropriate.

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

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

The description uses the specific verb 'Get' with the resource 'machine-readable instructions for adding a new part to the registry,' and enumerates the content areas (file layout, metadata schema, quality gates, PR process). This clearly distinguishes it from sibling tools like get_part and request_part, as it pertains to contributing rather than retrieving or requesting.

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

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

The description explicitly states 'Use when a part is missing,' providing a clear context for when the tool should be invoked. However, it does not explicitly name alternatives or exclusions, such as when to use request_part instead, so it falls 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?

No annotations are provided, so the description must carry the burden. It states the tool lists all parts and identifies return fields, but it does not disclose potential pagination, ordering, or scale behaviors. For a simple read-only list, this is adequate but not rich.

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 a single, efficient sentence that front-loads the action and includes key output fields. No wasted words.

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

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

For a tool with no parameters and no output schema, the description adequately explains that it lists all parts and specifies the returned fields. However, it omits any details about the result structure or potential volume/performance characteristics, which is acceptable given the simplicity.

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, which makes the schema coverage trivial. The description does not need to add parameter details; it correctly focuses on the output fields. Baseline 4 is appropriate.

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

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

The description clearly states 'List all parts in the registry' with a specific verb and resource, and enumerates the fields returned (id, name, family, pins). This distinguishes it from siblings like get_part (single part) and search_parts (filtered).

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

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

No explicit guidance on when to use this tool versus search_parts or get_part. The 'all parts' wording implies bulk listing, but there is no direct comparison or exclusion, so usage context 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?

No annotations are provided, so the description carries the behavioral disclosure burden. It reveals that feedback is recorded publicly on GitHub and contributes to a trust score, which is critical context for users who might otherwise assume private submission. It does not cover aspects like edit/delete limitations, but the provided details are meaningful.

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 compact yet rich, using two sentences with embedded real-world examples and an explicit invocation guideline. Every phrase earns its place, and the structure front-loads the core purpose before adding contextual details.

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, well-annotated tool, the description covers the necessary context: what the tool does, the public nature of the feedback, and when to use it. With no output schema, the description need not explain return values, and nothing essential is missing for an agent to invoke this 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 schema documents all three parameters. The description adds value by giving concrete examples for the 'notes' field and by clarifying that both 'worked' and 'problem' results are helpful, reinforcing the enum meaning 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 clearly identifies the action as reporting real-world usage feedback for a part, with concrete examples of what constitutes feedback. It distinguishes this from sibling tools like get_part, search_parts, and request_part by focusing on contribution of usage experience.

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

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

The description explicitly states when to use the tool ('after actually using a part') and encourages reporting both successes and problems. It does not explicitly name alternatives or when-not-to-use cases, but the context is clear enough to prevent misuse.

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 to provide safety/behavior hints, the description takes on the full burden. It discloses the background generation process, quality gates, and ~5-minute publication timeframe. This goes beyond the schema by describing the tool's internal behavior and outcome, though it doesn't mention failure modes or response structure.

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 dense but well-organized. Each sentence contributes meaning: purpose, process, family parameter mapping, and usage directive. It uses compact lists and abbreviations (e.g., 'pin_header_254 (2.54mm)') to avoid repetition. While longer than typical, the complexity of the tool justifies the length.

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 3 parameters, no output schema, and no annotations, this description is remarkably complete. It covers purpose, behavior, parameter usage, family options, timing, and which siblings to try first. The agent has enough information to decide when and how to invoke the tool 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?

The schema covers 67% of parameters, but the description adds substantial semantics: it maps family enums to specific parameters, provides pin count ranges (1-40), and lists exact variant codes for each family (e.g., ht73xx values). This helps the agent select valid parameter combinations far beyond the schema's generic 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?

The description clearly states the tool's purpose: 'Request on-demand generation of a parametric part that isn't in the registry yet.' This specific verb+resource combination distinguishes it from sibling tools like get_part and search_parts, reinforced by the closing directive 'Always try get_part / search_parts first.'

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 guidance is provided: 'Always try get_part / search_parts first' tells the agent when NOT to use this tool. It also details family-specific usage, indicating which parameter ('pins' vs 'variant') applies to each family, making when-to-use highly 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?

No annotations are present, so the description carries the burden. It discloses no login requirement, licensing, and that results include ids but not download URLs. It adds context about verified KiCad components and their composition, but lacks details on pagination or result limits.

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, each earning its place. The description is front-loaded with the core action and resource, followed by key constraints and a clear pointer to get_part, then practical examples. No wasted words.

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

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

For a simple search tool with one parameter and no output schema, the description sufficiently explains what is returned (matching parts with ids) and how to proceed (use get_part). Example queries cover diverse use cases. No critical information 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% for the single query parameter. The description adds value by providing concrete example queries ('usb-c', 'jst ph 4pin') and indicating that search covers names, families, keywords, and pin counts, reinforcing the schema's 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 clearly states 'Search the PartReel registry of verified KiCad components' with a specific verb and resource. It distinguishes from siblings by noting that get_part provides download URLs, implying search_parts is for discovery. Examples clarify the scope.

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 directs users to get_part for download URLs, identifying a key alternative. It implies use when searching for components and provides example queries, but does not explicitly contrast with list_parts or state when-not-to-use.

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?

Even without annotations, the description discloses the staging status, immediate searchability, hourly CI quality gates, and the auto-rejection of official library copies. This gives the agent a clear model of what happens on submission.

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 key outcome (SHARED IMMEDIATELY), then the promotion workflow and parameter guidance. No wasted words.

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

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

Given 9 parameters and no output schema, the description covers the input requirements and post-submission process adequately. It could mention the immediate response/return value, but the lifecycle is well explained.

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 description adds meaning to dimensions_source by requiring real datasheet citations and warns that official library copies are rejected, clarifying the expected quality of that parameter. It also explicitly mentions providing full file text for symbol and footprint, reinforcing those 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?

The description clearly states that the tool uploads a part for immediate sharing (status 'staging'), differentiating it from read-only siblings like get_part/search_parts and from request_part which asks for a part. The verb 'Upload' and specific resource 'part' are explicit.

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

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

The description implies use when you have built a part and want to share it, and even warns that copies of the official KiCad library will be auto-rejected, serving as a condition for acceptable submissions. It does not name alternative tools explicitly, but the sibling set and workflow make the context clear.

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