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mindmelt – B2B agency Frankfurt

find_cases

Filtert cases.json nach industry-ID und/oder exaktem capability-Feld.

industry: exakte oder teilweise industry-ID aus cases.json (z.B. "b2b-werbeagentur").
capability: Suchbegriff, der exakt gegen das capability-Feld jedes Case gematcht wird
    (kein Fallback auf das services-Array).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
industryNo
capabilityNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
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 burden. It does add real behavioral value by disclosing the matching semantics: industry supports exact or partial matching, while capability is matched exactly with no fallback to the services array. However, it says nothing about return format, permissions, or result limits.

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 core action in the first sentence, then documents the two parameters compactly. No redundant filler, though the parenthetical caveat about services-array fallback could be tighter.

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

Completeness3/5

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

For a two-parameter filter tool with no annotations and no output schema, the description explains matching semantics but leaves the return shape (list of matching cases, fields returned, ordering) entirely unstated. Adequate but with a clear gap.

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?

Schema description coverage is 0% (bare string properties with only titles), so the description must compensate and largely does: it explains that industry accepts exact or partial IDs (with an example) and that capability is an exact-match term against the capability field. It adds meaning well 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+resource: filtering cases.json by industry-ID and/or capability field. An agent can tell it targets the cases dataset, but it never names or contrasts with siblings like search_services or find_pages, so the differentiation is only implicit.

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 'und/oder' phrasing implies you can filter by either or both fields, which is usable guidance, but there is no explicit when-to-use-this-vs-alternatives or exclusion criteria (e.g. when to prefer search_services instead).

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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