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eData4You MCP Server

submit_lead_request

Protected tool. Submit a sanitized lead request into server logs for follow-up. Requires MCP write authorization.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesContact name.
emailYesContact email.
phoneNoOptional phone or WhatsApp.
companyNoCompany or brand name.
messageYesProject details.
serviceYesRequested service.

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already indicate the tool is not read-only and not destructive. The description adds value by clarifying the write operation ('submit'), the sanitization step, and the authorization requirement, providing behavioral context 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.

Conciseness5/5

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

The description consists of two short, front-loaded sentences with no superfluous words. Every phrase ('Protected tool', 'sanitized', 'server logs for follow-up') contributes essential information.

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

Completeness4/5

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

For a simple submission tool, the description provides clear purpose, authorization needs, and behavior. No output schema is expected, and the annotations cover safety. The description is complete enough for an agent to use correctly, though it omits details like error handling or success response.

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

Parameters3/5

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

Schema coverage is 100% and includes descriptions for all 6 parameters (e.g., 'name', 'email', 'phone'). The description does not add new semantic meaning beyond stating which parameters are required (name, email, service, message), which is already captured in the schema's required array.

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 clearly states the action ('submit'), the resource ('sanitized lead request'), and the destination ('server logs for follow-up'). This is a specific verb+resource combination that distinguishes it from the sibling tools, none of which handle lead submission.

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

Usage Guidelines4/5

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

The description indicates it is a 'Protected tool' and 'Requires MCP write authorization', setting clear access requirements. While it does not explicitly contrast with alternatives, the sibling list contains no similar lead submission tool, making the usage context self-evident.

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

B3.2/5.0
Disambiguation4/5

Most tools have distinct purposes (e.g., blog_generator vs. get_blog_post), but there is overlap among some content validation tools (csv_cleaner, duplicate_sku_finder, inventory_checker, product_catalog_validator) which could cause confusion. Also, compare_services and search_services serve similar functions.

Naming Consistency3/5

Tool names mix styles: some use noun_verb (amazon_listing_manager), others verb_noun (compare_services), and there are multiple prefixes like get_, search_, list_. This inconsistency may hinder an agent's ability to predict tool names.

Tool Count2/5

With 35 tools, the set is large and covers many subdomains. This could overwhelm an agent, making selection challenging. A more focused subset would improve coherence.

Completeness4/5

The tools cover a broad range of ecommerce operations: listing management for multiple platforms, content generation, data validation, research, and reporting. However, missing update/delete capabilities and some platform interactions limit full lifecycle coverage.

Resources