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akib-dabgar

Akib Dabgar MCP Server

by akib-dabgar

query_profile

Query Akib Dabgar's professional profile, skills, experience, projects, or interests using natural language questions. Returns only relevant sections based on your query.

Instructions

Query Akib Dabgar's professional profile, skills, experience, projects, or interests. Uses Dynamic Context Retrieval to return only the relevant sections matching the query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe natural language question or keywords regarding Akib Dabgar (e.g., "What databases does Akib know?", "Tell me about ChainIT Pay").
Behavior3/5

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

The description mentions 'Dynamic Context Retrieval' and that it returns only relevant sections, disclosing a key behavioral trait. However, with no annotations provided, the description does not cover other behavioral aspects like auth needs, rate limits, or potential side effects, so it's only partially transparent.

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 two sentences long, with no redundant information. Every word contributes to clarity. It is front-loaded with the core purpose and efficiently adds behavioral detail.

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 (one parameter, no output schema, no siblings), the description fully covers what the tool does and how it behaves. It explains the retrieval mechanism and the scope of results, making it complete for an AI agent to invoke correctly.

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 already describes the 'query' parameter as a natural language question. The description adds concrete examples ('What databases does Akib know?', 'Tell me about ChainIT Pay'), which enhance understanding beyond the schema. Since schema coverage is 100%, the baseline is 3, and the examples justify a 4.

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 specifies the verb 'Query' and the resource 'Akib Dabgar's professional profile'. It lists the specific aspects queried (skills, experience, projects, interests), making the purpose unambiguous and distinct from any potential siblings.

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 asking natural language questions about Akib Dabgar, but it provides no explicit guidance on when to use this tool versus alternatives or when not to use it. Since no sibling tools exist, the lack of exclusions is acceptable but still leaves room for improvement.

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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