Raintree Technology

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

Annotations already declare this is read-only and non-destructive. The description adds useful scope context by saying the information is public and enumerates what it covers, but it does not add detail about response shape or operational behavior. With annotations present, this is adequate but not exceptional.

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, front-loaded with the use case, and no filler. Every word contributes either to when to use the tool or what it returns.

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 tool with an output schema, the description is complete. It states the company, the nature of the data, and the exact use condition. 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?

The tool has zero parameters, so the baseline is 4 and the description does not need to explain parameter behavior. The description's content focus is sufficient given there is nothing to configure.

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 a specific verb ('get') and resource ('Raintree Technology's public company description, sectors, and portfolio scope'). This clearly distinguishes the tool from siblings like get_project or list_products, whose scopes are different.

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 gives the triggering condition: 'Use this when an agent needs to get' the specified company capabilities. It does not name exclusions or alternatives, but the context is clear and the tool has no parameters, so no stronger selection guidance is needed.

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 the call read-only and non-destructive; the description adds contextual behavior that no message or form submission occurs, which is meaningful because the tool name could suggest outreach. It does not introduce any contradiction or unneeded risk information.

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, immediately states when to use it, and contains no redundant clauses. Every word contributes.

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 tool with an output schema and readOnly/destructive annotations, the description covers the key selection criteria and side-effect behavior. Nothing essential 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?

Input schema has zero properties, so there are no parameters to document. Baseline for zero-parameter tools is 4; the description adds no param details because none are 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?

States a specific verb ('return') and resource ('public contact options'), and distinguishes the operation from sending messages or submitting forms. It is clearly distinct from sibling tools that return capabilities, projects, products, or writing.

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 an explicit trigger: use it when an agent needs to return public contact options. It implies the negative case—do not use for sending messages or form submission—but does not name an alternative tool by name.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so no side-effect warnings are needed. The description adds the 'public' scope, which lightly implies broader accessibility, but it does not disclose behavior 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?

The description is a single, front-loaded sentence with no filler. It communicates the tool's purpose and the key precondition ('by canonical identifier') efficiently.

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 tool has one fully documented parameter, a read-only annotation, and an output schema. The description, combined with the structured metadata, is sufficient for an agent to select and call 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 already documents the only parameter with 'Canonical public project identifier', and the description reinforces this. With 100% schema description coverage, the description adds no additional semantic value.

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 a specific verb ('get'), a concrete resource ('one public Raintree Technology project'), and the lookup mechanism ('by canonical identifier'). This clearly separates it from sibling tools like search_projects and list_open_source_projects.

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 states when to use the tool: when an agent needs a single public project identified by its canonical ID. It does not name alternative tools or explicitly state when not to use it, but the conditionality is clear enough.

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 establish readOnlyHint=true and destructiveHint=false. The description adds useful scope context by specifying 'public open-source' and confirms this is a listing operation rather than a mutation. No hidden side effects are implied.

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 sentence with a front-loaded usage trigger and no filler. Every word contributes to understanding what the tool does and when to use it.

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, an available output schema, and annotations covering the safety profile, the description fully specifies what the tool lists. An agent has enough context to select and 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?

The input schema is empty with 0 parameters, so parameter documentation is not needed. This matches the 0-parameter baseline; the description correctly avoids inventing parameter details.

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 (list), a specific resource (Raintree Technology's public open-source projects and repositories), and an explicit scope ('public'). This clearly distinguishes the tool from siblings like get_project and search_projects.

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 opens with 'Use this when an agent needs to list...', giving a clear trigger condition. It does not explicitly name alternatives or state when not to use it, but the context is clear enough for a parameterless listing tool.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds the qualifier 'public', which implies no authentication/scoping needs, but it does not describe pagination, result structure, or any other behavioral traits. The output schema likely covers return values, keeping this at an adequate 3.

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 sentence that is front-loaded with the trigger condition and fully states the tool's purpose. There is no redundant wording or fluff, making it optimally concise for a no-parameter tool.

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 there are no parameters, a straightforward purpose, and an existing output schema, the description is fully sufficient for an agent to understand what the tool does and invoke it appropriately. Nothing critical is missing.

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

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

The tool has zero parameters, so the baseline is 4. The description does not need to compensate for parameter documentation because there is nothing to document; calling the tool requires no inputs.

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

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

The description states a specific verb and resource: 'list Raintree Technology's public products.' It clearly indicates the scope ('public') and the domain (Raintree Technology), which distinguishes it from sibling tools like list_open_source_projects or search_projects.

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 opens with an explicit use case: 'Use this when an agent needs to list...'. It provides clear context for when to invoke the tool but does not mention exclusions or explicitly differentiate from sibling alternatives. Since there are no parameters and the sibling tools cover different domains, this is a minor gap.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds context by labeling the search scope as 'public', but it does not disclose additional behaviors such as matching semantics, result limits, or sorting. With annotations present, a 3 is appropriate.

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, front-loaded sentence that directly states when to use the tool. It contains no filler or redundant information, making it highly efficient.

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 tool with one well-documented parameter, an output schema, and clear read-only annotations, the description covers the necessary invocation context. It could mention what kind of results are returned, but the output schema already covers return structure, so completeness is strong.

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% for the single query parameter, which is fully documented as a case-insensitive public search query. The description adds minimal semantic value beyond the schema, meeting 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 clearly states the tool is for searching Raintree Technology's public product and open-source portfolio, giving a specific resource and action. It implicitly distinguishes itself from search_writing (which searches writing) and from list tools, though it does not explicitly name a sibling.

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 begins with 'Use this when an agent needs to search...' which gives clear context for invocation. It does not provide when-not-to-use guidance or mention alternative siblings like list_open_source_projects or list_products, so it stops short of a 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds the useful 'public' scope but does not disclose other behavioral traits such as result limits or corpus coverage. This is adequate but not rich for a search 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?

A single front-loaded sentence that immediately states the use case and resource. No filler or redundant explanation; every word 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 one-parameter, read-only search tool with an output schema and complete parameter docs, the description supplies the essential when-to-use context. It could mention an alternative to search_projects explicitly, but nothing critical is missing 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%: the single query parameter is documented with case-insensitivity and length limits. The tool description does not add parameter-level meaning beyond the broader corpus scope, 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?

Description names a specific verb ('search') and resource ('public Raintree Technology writing and research'), which clearly differentiates it from the sibling search_projects (projects vs writing/research). Even without opening the schema, an agent knows exactly what corpus this tool targets.

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 opening phrase 'Use this when an agent needs to search public Raintree Technology writing and research' gives an explicit trigger condition. It does not name alternatives or exclusions, but the corpus is distinct enough from search_projects that the main choice is clear.

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