Skip to main content
Glama
Sign In
aka-recht

aka-recht

by Cehha79
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

dokument text

dokument_text
Read-only

Extracts text excerpts from case documents (Word, email, PDF, HTML) and shows source (direct, PDF layer, scan) and reading quality. Verify figures and deadlines at original.

Instructions

Textauszug eines Dokuments (Word, E-Mail, PDF, Text, HTML) mit Herkunft: textquelle sagt, ob der Text direkt, aus der PDF-Textschicht oder gar nicht gelesen wurde (Bildscan, Foto); textstand ist die in der Akte vermerkte Lesequalität. Der Auszug ist eine Ableitung, Zahlen und Fristen am Original prüfen.

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": {
      "fall": "<fall>",
      "dokument": "<dokument>"
    },
    "name": "dokument_text"
  }
}

Input Schema

NameRequiredDescriptionDefault
fallYes
dokumentYesD-Kennung wie D0038

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare this is a safe, non-destructive, closed-world read. The description adds genuinely useful behavioral context beyond that: it explains the provenance fields (textquelle = direct / PDF layer / not read at all, e.g. image scan; textstand = recorded reading quality) and flags that the excerpt is derived and may be unreliable for numbers and deadlines.

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 what the tool returns and then explains the provenance fields, with the verification caveat last. It is one dense run-on sentence, but every clause carries information and nothing is redundant.

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?

With no output schema, the description does the work of explaining the return shape (textquelle, textstand) and the reliability caveat, which is exactly what an agent needs to interpret the result. The remaining gap is the undocumented 'fall' parameter.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 50% — only 'dokument' is described (as a D-Kennung like D0038). The description adds no meaning to either parameter; 'fall' is entirely undocumented in both schema and description, and the discussion of textquelle/textstand refers to return fields, not inputs.

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 (extracts a text excerpt) and resource (a document), and enumerates supported formats (Word, E-Mail, PDF, Text, HTML). It is clearly distinguishable from siblings like texterkennung (OCR) and dokumente_suchen (search), since this tool reads one document's text with provenance.

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?

Usage is implied — read the text of a given document — and the description usefully warns that the excerpt is a derivative and numbers/deadlines should be verified at the original. But it names no explicit alternative or when-not condition relative to texterkennung or dokument_ordnen, so routing relies on inference.

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

Deploy Server

Other Tools