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

With no annotations provided, the description carries the full burden of disclosure. It goes beyond a simple listing by describing the return structure (JSON array of id/name/description), explicitly listing the covered platforms, and adding performance context ('This tool is cheap (no OpenAI call, no vector search)'). It also explains the relationship between returned ids and sti_search's platform parameter. It doesn't mention error handling or rate limits, but for a read-only list tool, the provided behavioral details are substantial.

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 well-structured and front-loaded. The first sentence gives the core purpose instantly. Subsequent sentences add only essential details: return format, when to call, and cost behavior. Each paragraph earns its place, and the tone is directive and economical without being terse.

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 is remarkably complete. It covers what the tool returns, the exact shape of the return objects, the platforms included, the intended invocation order relative to sti_search, and cost implications. There are no missing pieces that would leave an agent uncertain about how or when to invoke 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?

The tool has zero parameters, and the input schema is an empty object, so the description doesn't need to explain parameters. Per the rubric, the baseline for 0 params is 4. The description adds extra semantic value beyond the schema by stating that the returned platform ids are 'the exact strings accepted by the platform parameter of sti_search,' which helps the agent understand how to use this tool's output as input elsewhere.

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 function: 'List every Stimulsoft product/platform that has indexed documentation available through this MCP server.' It specifies the verb (List), the resource (Stimulsoft products/platforms), and the return format (JSON array of { id, name, description }). It also distinguishes itself from the sibling sti_search by explaining its role as a platform-listing tool, not a search 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?

The description provides explicit usage guidance: 'CALL THIS FIRST when the user's question is ambiguous about which Stimulsoft platform they are using, or when you need to pick a valid platform value to pass into sti_search.' It also notes when it is safe to call freely ('call it freely whenever you are unsure about platform naming') and explains how its output connects to the sibling tool, making the when-to-use vs. alternatives crystal 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?

With no annotations provided, the description fully discloses behavior: it returns a ranked JSON array with specific fields, contains full Markdown content, handles cross-lingual queries, and warns that training data may be out of date, requiring mandatory tool usage. It also explains the embedding/cosine similarity mechanism and low-score retry behavior.

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 longer than average but every section earns its place, and it is well-structured with bolded lead-ins (USE THIS TOOL, SEARCH STRATEGY, DO NOT USE, IMPORTANT). The main purpose and output format are front-loaded in the first two sentences, and the rest serves as necessary usage context for a complex 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 no output schema, so the description carries the full burden of explaining return values—it does so explicitly. It also covers input nuances (language, platforms, categories), references the sibling tool, provides a retry strategy, and notes the importance of using the tool over the model's own knowledge, making the description self-contained.

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?

Although schema coverage is 100%, the description adds significant meaning beyond the schema: examples of good vs bad queries, instruction to pass queries verbatim without translation, guidance on when to use limit (3–5 vs 10–20), and clarification that `platform` should be omitted or prefixed with a `sti_get_platforms` call if uncertain.

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 opens with a specific verb+resource combination: "Authoritative semantic search over the official Stimulsoft Reports & Dashboards developer documentation" and details the return format. It clearly distinguishes from the sibling tool `sti_get_platforms` by positioning itself as the search operation and even referencing the sibling for platform discovery.

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 includes explicit when-to-use criteria (a bulleted list of Stimulsoft-related topics) and when-not-to-use (non-Stimulsoft libraries, general reporting theory). It also names the alternative `sti_get_platforms` and provides a detailed SEARCH STRATEGY with fallback and reformulation guidance.

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