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mindmelt – B2B agency Frankfurt

search_services

Volltextsuche (Keyword/Alias-Matching, kein Vector-Search) ueber capabilities.json und services.json inkl. aliases aus entities.json.

query: Suchbegriff(e), z.B. "SEO Relaunch" oder "KI Sichtbarkeit".
Gibt sortierte Treffer aus capabilities und services mit Score zurueck.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It does disclose real behavioral traits: the matching mode is keyword/alias rather than vector search, and results are sorted with a score across capabilities and services. However, it says nothing about authentication, result limits, or pagination, leaving gaps for a search tool.

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?

Three short lines, front-loaded with the operation and scope, followed by parameter guidance and return content. Little waste, though the line breaks make it slightly list-like rather than prose.

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?

No output schema exists, and the description partly compensates by stating results are sorted hits from capabilities and services with a score. Combined with the clear search scope and parameter example, an agent has enough to invoke it correctly; only pagination/limits are unaddressed.

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% and the single query parameter has no schema-level documentation, so the description must compensate. It does so by defining query as Suchbegriff(e) and giving concrete examples ("SEO Relaunch", "KI Sichtbarkeit"), which clarifies the expected input format.

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?

States a specific verb (Volltextsuche) and the exact resources searched (capabilities.json, services.json, aliases from entities.json), plus a negative scope (kein Vector-Search). An agent can distinguish this keyword/alias matcher from siblings like find_pages or get_capability without opening any schema.

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 when the tool applies (broad keyword lookup over capabilities/services/aliases) and rules out vector-search semantics, but never names an alternative sibling or states an explicit when-not condition or prerequisite. Usage is inferable rather than guided.

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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