mindmelt – B2B agency Frankfurt

recommend_services

Matched eine Problembeschreibung gegen die 'problem'-Felder in services.json und liefert die am besten passenden Services samt verknuepfter capabilities.

problem: Freitext-Problembeschreibung, z.B. "B2B-Relaunch mit SEO und KI-Sichtbarkeit".

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": {
      "problem": "<problem>"
    },
    "name": "recommend_services"
  }
}

Input Schema

Name	Required	Description	Default
`problem`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 2, 2026

TDQS

B3.1/5.0

Behavior2/5

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 does not disclose matching algorithm, ranking behavior, result limits, whether multiple services are returned, or error behavior. It mentions linked capabilities, which is a small plus, but far more disclosure is needed for a recommendation tool with no annotation coverage.

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?

Two short parts: one sentence for the core behavior and one for the parameter with an example. No waste, front-loaded purpose. Slightly informal formatting due to line break, but efficient.

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 recommendation tool with no output schema and no annotations, the description omits critical context: how matching works, what a 'service' object contains, ranking, and whether it requires exact terms. It is incomplete for the tool's complexity.

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%, so the description must compensate. It does explain the single parameter as a free-text problem description and gives an example (B2B relaunch with SEO and AI visibility). This is helpful but minimal given 0% schema coverage; the baseline of 4 for zero params does not apply since there is one param.

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+resource: matches a problem description against 'problem' fields in services.json and returns best-matching services plus linked capabilities. This distinguishes it from search_services, though the mechanism (fuzzy keyword matching) is not fully specified. Clear purpose overall.

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 when you have a free-text problem statement, but does not explicitly state when to use this versus search_services, find_cases, or check_fit. Context is implied rather than spelled out.

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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