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List Products

list_products
Read-onlyIdempotent

List every product in the Research Desk line with its included skills. FREE.

Takes no arguments. Returns a list of 7 product objects, each {"slug": "thesis-advisor", "name": ..., "tagline": ..., "skills": ["Skill A", ...], "free_skill": "Gateway Skill Name"}. Use the returned slug values with get_free_skill, get_full_product, or get_full_skill.

Returns metadata only - no persona text and no skill instructions. Use when the caller wants to see what this server covers. Not for keyword search across the whole 138-product catalog, which the catalog server's search_catalog does, and not for instructions the caller can act on (get_free_skill). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds significant behavioral context beyond these: it explains that the tool never raises a protocol error but returns an error object, and that after correcting input it is always safe to retry. This provides error-handling and retry safety details that annotations do not cover.

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

Conciseness5/5

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

Every sentence in the description serves a purpose: announcing the product line, confirming no arguments, detailing the return structure, explaining usage, and describing error behavior. The information is front-loaded and logically organized. There is no redundancy or fluff.

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

Completeness5/5

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

Given the tool's simplicity (zero parameters, no required inputs, and an output schema not shown but referenced), the description covers all relevant aspects: purpose, parameters, output structure, usage guidance, error handling, and comparisons with sibling tools. It is fully self-contained and leaves no gaps.

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

Parameters4/5

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

The input schema has zero parameters, so the description's statement 'Takes no arguments' confirms the schema. The baseline for 0 params is 4. The description also adds context about the return structure, which indirectly helps the agent understand the tool's output, though it does not add parameter-specific semantics. Still, it is clear and sufficient.

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

Purpose5/5

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 every product in the Research Desk line with its included skills.' It also distinguishes itself from sibling tools like get_free_skill, get_full_product, and get_full_skill by explaining that it returns metadata only, not instructions or persona text. This is a clear and differentiated purpose.

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

Usage Guidelines5/5

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

The description explicitly tells when to use the tool: 'Use when the caller wants to see what this server covers.' It also provides clear exclusions: 'Not for keyword search...which the catalog server's search_catalog does, and not for instructions the caller can act on (get_free_skill).' Moreover, it directs the user to use the returned slug values with get_free_skill, get_full_product, or get_full_skill, providing explicit alternatives.

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

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TDQS

A4.3/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: statistical planning (sample_size), description (stats_describe), interval estimation (confidence_interval), citation formatting (format_citation), and product/skill retrieval (list_products, get_free_skill, get_full_skill, get_full_product). No two tools overlap in function, and the descriptions explicitly clarify boundaries.

Naming Consistency3/5

Naming is a mix of verb_noun (list_products, get_free_skill, format_citation) and descriptive noun phrases (confidence_interval, sample_size, stats_describe). While all are readable and use snake_case, the lack of a consistent pattern (e.g., all verbs or all nouns) makes it harder to predict tool names.

Tool Count4/5

At 8 tools, the count is appropriate for the server's scope, which covers statistics, citation formatting, and product retrieval. It is not overburdened, and each tool seems justified. The number is slightly above the minimal threshold but well within a reasonable range.

Completeness2/5

The server's name 'research' suggests broader coverage, but the tool surface has notable gaps. Basic statistical tools like hypothesis tests (t-test, ANOVA), correlation, or proportion analysis are missing. The citation tool is limited to three styles. The product retrieval tools are tied to a specific product line, leaving a weak general research focus.

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