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Expertise MCP Server

Expertise MCP Server

by Alicanefee
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

ask_cv

Ask natural-language questions about Ali Can Efe's CV to get structured answers on his experience, education, skills, or speaking engagements.

Instructions

Ask a natural-language question about Ali Can Efe's CV — experience at Canon Medical Systems, education (Brunel University London MSc Biomedical Engineering, Işık University BSc Electrical-Electronics Engineering), skills, or speaking engagements. Returns a structured answer. Use when user asks about Ali's background, employment history, or qualifications.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "question": "<question>"
    },
    "name": "ask_cv"
  }
}

Input Schema

NameRequiredDescriptionDefault
questionYesNatural-language question (e.g. 'What did Ali do at Carestream?')

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It adds only 'Returns a structured answer', with no detail on permissions, accuracy limits, or what the structured response contains. The read-only lookup nature is only implied by the phrasing.

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?

Front-loads the purpose, then parenthetically lists coverage domains, then a usage cue — no wasted sentences. The long enumerated topics make the middle clause dense but they earn their place by scoping what can be asked.

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 one-parameter natural-language QA tool with no output schema and no annotations, the description covers purpose, scope, and trigger condition adequately. The only real gap is the unspecified shape of the 'structured answer'.

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?

With a single parameter at 100% schema description coverage, the schema already documents the 'question' input including an example. The description reinforces the expected subject matter but adds no formatting or syntax guidance beyond the schema.

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?

States a specific verb ('Ask') and resource (Ali Can Efe's CV) and enumerates the covered domains — experience, education, skills, speaking engagements. It is clearly distinguishable from siblings like get_projects or query_expertise by topic, though it never names an alternative explicitly.

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?

'Use when user asks about Ali's background, employment history, or qualifications' gives a clear triggering context. There are no when-not conditions or named alternatives, so it stops short of full routing guidance.

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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