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order_market_research

Order a professional market research report (1,500+ words, same-day delivery). Pay $49 USDC on Ethereum mainnet.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailYesEmail address to receive the report
topicYesThe market or industry to research
languageNoEnglish

TDQS

A3.6/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses payment requirement ($49 USDC on Ethereum mainnet), delivery speed (same-day), and output size (1500+ words). This is substantial transparency for a payment-required tool, though it omits confirmation steps or failure handling.

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?

The description is a single, efficient sentence that includes all essential information: purpose, output specification, delivery, and payment. Every word contributes meaning, with no redundancy or filler.

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

Completeness3/5

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

Given 3 parameters, no output schema, and no annotations, the description covers the core purpose but lacks details on output format, payment flow, error scenarios, or post-order steps. For a simple ordering tool, it is adequate but not fully comprehensive.

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?

Schema coverage is 67% (2 of 3 params have descriptions). The description adds value beyond the schema by specifying report quality (1500+ words, professional) and payment details, which are not in parameter descriptions. This compensates well for the missing language description.

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

Purpose4/5

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

The description clearly states the tool orders a market research report with specifics (1500+ words, same-day delivery, $49 USDC). However, it does not explicitly differentiate from sibling tools like order_blog_post or order_competitor_analysis, though the 'topic' parameter implies market research context.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, limitations, or when not to use it. The agent would have to infer usage from the tool name and description content.

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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Glama MCP Gateway

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TDQS

A4/5.0
Disambiguation5/5

Each tool has a distinctly different deliverable (blog post, competitor analysis, market research, technical summary), making it easy for an agent to select the appropriate one.

Naming Consistency5/5

All tool names follow a consistent 'order_<noun>' pattern, which is predictable and clear.

Tool Count5/5

With 4 tools, the server is well-scoped for its purpose of ordering research reports; not too few or too many.

Completeness4/5

Covers common report types (blog, competitor, market, technical) but lacks options for custom requests or status tracking, though these are minor gaps for the intended scope.

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