Freelance MCP Server

research_client_intel

Analyze client data to assess quality and reliability, delivering a detailed intelligence report for better freelance decisions.

Instructions

Research client quality and reliability

Args:
    client_data: Client information (id, rating, reviews, total_spent, etc.)

Returns:
    Detailed client intelligence report

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": {
      "client_data": "<client_data>"
    },
    "name": "research_client_intel"
  }
}

Input Schema

Name	Required	Description	Default
`client_data`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.1.0Sep 13, 2026

TDQS

C2.7/5.0

Behavior1/5

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

No annotations are provided, so the description carries the full burden of disclosing behavioral traits. It says nothing about permissions, whether the operation is read-only, rate limits, or side effects; it only states that a report is returned.

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 short and front-loads its purpose, followed by a compact Args/Returns structure. It contains no filler, though the return line is redundant given the existing output schema.

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?

For a tool accepting a nested client_data object with 0% schema description coverage, the description is incomplete: it does not explain expected input fields, required subfields, or usage context. The output schema makes the return description unnecessary, but the input side is underspecified.

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 0%, and the only parameter is a nested object with no schema detail. The description partially compensates by listing example fields ('id, rating, reviews, total_spent, etc.'), adding some meaning beyond the bare schema, but the required structure and subfields remain vague.

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 ('Research') and resource ('client quality and reliability'), making the tool's broad purpose clear. It does not explicitly differentiate itself from siblings like analyze_profile_fit or validate, 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 Guidelines2/5

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

The description gives no guidance on when to use this tool versus alternatives, no prerequisites, and no contextual triggers. Usage is only implied by the tool name and purpose.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server