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mcp-server

by com.interzoid

interzoid_custom_data

Destructive

AI Custom, Self-Defined Data Enrichment. Define your own data API: describe a topic, provide a lookup value, and specify the output fields you want. Returns real-world data as a JSON object with your self-defined fields. Example: topic='detailed information about companies', lookup='IBM', output='["headquarters","ceo","website"]'. Premium API, cost varies by model.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoAI model to use (optional: 'default', 'model-a', 'model-a-premium', 'model-x')
topicYesDescription of the data domain (e.g. 'detailed information about companies', 'healthcare data by city')
lookupYesThe lookup value to retrieve data for (e.g. 'IBM', 'Las Vegas', '85250')
outputYesJSON array of desired output field names (e.g. '["headquarters","ceo","website"]')

TDQS

A4.2/5.0
Behavior4/5

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

Annotations include destructiveHint=true and openWorldHint=true. The description adds context about cost implications ('Premium API, cost varies by model') and the custom nature of data, providing transparency beyond annotations. It does not contradict 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 is concise: two sentences plus an example. It is front-loaded with the most important information and wastes no words.

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?

Given the tool's complexity (4 parameters, no output schema), the description covers the essential: purpose, parameters, example, and cost. It could include error handling or response format details, but it is adequate for understanding the tool's use.

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 description coverage is 100%, so the baseline is 3. The description adds an example and clarifies that 'output' is a JSON array, but this is minimally additive to the schema descriptions. The parameters are well-documented, but the description does not significantly enhance meaning.

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 tool's purpose: 'Define your own data API' with specific parameters (topic, lookup, output). It distinguishes itself from sibling data enrichment tools by emphasizing custom, self-defined fields and provides an illustrative example.

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 explains the tool's usage through an example and notes it's premium and cost varies. However, it lacks explicit guidance on when to use versus alternatives (e.g., when you need predefined data vs. custom fields) and does not mention exclusions.

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

B3.4/5.0
Disambiguation4/5

Most tools target distinct data operations (matching, standardization, enrichment) with clear descriptions. Minor overlaps exist, e.g., address_match_advanced vs global_address_match, but descriptions differentiate them.

Naming Consistency5/5

All tools follow a consistent 'interzoid_descriptive_function' pattern in snake_case, making it easy to predict purpose from the name.

Tool Count3/5

58 tools is high for a single server, exceeding the typical 3-15 range. While each serves a specific data enrichment function, the quantity may overwhelm agents without clear categorization.

Completeness4/5

The tool surface covers a broad domain including address, company, person, and financial data. Minor gaps exist (e.g., no reverse IP lookup, limited social media coverage), but core data needs are well-addressed.

Resources