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Glama

dilli-email-validation-api-server

Server Details

An MCP server that provides tools to validate an email address using Dilli Email Validation API.

Status
Healthy
Last Tested
Transport
Streamable HTTP
URL

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Tool DescriptionsC

Average 2.9/5 across 1 of 1 tools scored.

Server CoherenceA
Disambiguation5/5

With only one tool, there is absolutely no possibility of confusion between tools. The purpose is clear and singular.

Naming Consistency5/5

A single tool named 'validate_email' follows a clear verb_noun pattern and is internally consistent. There are no conflicting naming conventions to evaluate.

Tool Count4/5

The server has only one tool, which is slightly below the typical 3-15 range, but it is entirely appropriate for a narrowly focused email validation service. The count feels reasonable rather than inadequate.

Completeness4/5

The server covers the core email validation use case completely. Minor gaps like batch validation or additional email-related utilities are not present, but for a single-purpose API wrapper, the surface is adequate.

Available Tools

1 tool
validate_emailCInspect

Validates an email address using the DEVA API.

ParametersJSON Schema
NameRequiredDescriptionDefault
emailYes
Behavior2/5

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

With no annotations, the description carries the full burden of disclosing behavior. It does not mention whether the operation is read-only, what happens with invalid emails, or the return format. The only information is that it uses the DEVA API, which does not clarify side effects or outputs.

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

Conciseness4/5

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

The description is a single sentence with no unnecessary words, making it highly concise. However, it is under-specified, which harms its overall value, but for conciseness alone it earns a 4.

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

Completeness2/5

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

The tool is simple (one parameter), but there is no output schema and no annotations. The description does not explain what the tool returns or any behavioral nuances, leaving the agent without critical information about the outcome of the validation. This is incomplete for an agent to use effectively.

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

Parameters2/5

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

The schema has one parameter 'email' with no description, and the tool description does not add any meaning beyond the parameter name. Coverage is 0%, so the description should compensate, but it merely restates the concept of an email address without adding format constraints or usage details.

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 states a specific verb ('validates') and resource ('email address'), making the tool's purpose clear. However, it does not elaborate on what kind of validation is performed (e.g., syntax, deliverability), so it falls short of a 5.

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

Usage Guidelines3/5

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

The description implies usage for validating email addresses but provides no explicit guidance on when to use this tool versus alternatives or any exclusions. Since there are no sibling tools, the absence of comparisons is acceptable, but the lack of contextual usage cues keeps it at a 3.

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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