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, openWorldHint=false, and destructiveHint=false, so the safety profile is fully covered. The description adds no behavioral context beyond that—no scope, pagination, or count limits for the list, and "related" is left undefined.

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

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

A single front-loaded sentence with no filler; the trigger condition leads. It is slightly padded by the phrase "an agent needs" and the ambiguous "related" qualifier, but nothing is wasted.

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

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

With zero parameters, full annotation coverage, and an output schema, the description has little structural burden. Still, what makes a product "related" and how this differs from list_open_source_projects or get_project is left unanswered, which is a real gap for selection.

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

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

The tool takes zero parameters, so per the rubric the baseline is 4. There is nothing for the description to disambiguate, and it does not introduce confusing pseudo-parameters.

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?

It states a specific verb (list) and resource (products) and scopes them to one vendor's founder. However, "related products" is an ambiguous qualifier, and the description never distinguishes this list from the sibling list_open_source_projects, which also enumerates a product-like collection.

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

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

It gives a trigger condition ("when an agent needs to list..."), which is more than nothing, but provides no when-not guidance and never names the sibling tool to use for adjacent product/project queries. Usage is implied rather than routed.

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, destructiveHint=false, and openWorldHint=false, so the safety profile is covered structurally. The description adds nothing behavioral beyond that — no mention of result limits, pagination, or the closed-world scope that would help an agent predict what comes back.

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

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

A single front-loaded sentence with no filler, so nothing is wasted. It is efficient but so thin that the brevity borders on under-specification rather than tight communication.

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?

An output schema exists and annotations cover the safety profile, so return values and side-effects need not be restated. What is missing is the routing information — when to pick this over the many sibling search/list tools — which leaves the definition minimally adequate.

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

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

Schema description coverage is 100% and the single required query parameter is fully documented in the schema (case-insensitive, 1-200 chars). The description adds no syntax, format, or matching-behavior detail, so baseline 3 is appropriate.

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

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

The description gives a specific verb (search) and resource (Raintree Technology's open-source tools and related founder products), which is clearer than the bare name. However, it does not distinguish this search tool from sibling listers like list_open_source_projects or list_products, leaving the boundary somewhat implicit.

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?

"Use this when an agent needs to search..." simply restates the tool's own function rather than giving real usage criteria. It offers no when-not guidance and never names or contrasts the sibling tools (list_open_source_projects, list_products, search_research) that an agent must choose between.

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, covering the safety profile. The description adds the constraint that only public research is searched, which is a useful scope detail not present in annotations. However, it does not disclose any other behaviors like result limits, pagination, or authentication requirements, leaving the description to add minimal value beyond the annotations.

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

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

The description is a single, focused sentence that front-loads the action and purpose. There is no filler or redundant information; every word earns its place. It is appropriately concise for a simple search 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?

The tool has a simple signature with one parameter and an output schema. The description states purpose and usage context, and annotations cover the safety profile. It does not explain the result format, but the output schema likely handles that. It is sufficient for an agent to invoke correctly, though it could mention limitations like the scope of 'research'.

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 provides 100% coverage for the single 'query' parameter, including a description that it is case-insensitive. The tool description adds no additional parameter details, so it does not enhance the schema's documentation. With high schema coverage, 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?

The description clearly identifies the action ('search') and the target resource ('public Raintree Technology research'). However, it does not distinguish this tool from the sibling 'search_projects', which also searches but may cover a different scope. The purpose is clear but not uniquely differentiated.

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: 'when an agent needs to search public Raintree Technology research.' This provides a clear context, but it does not mention alternatives or exclusion cases, such as when to use 'search_projects' instead.

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