legal_get_statsGet database and search index statistics including counts of laws, norms, and indexed vectors. Use for health checks or to understand the scope of the legal corpus.
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds context by specifying what statistics are returned (counts of laws, norms, indexed vectors) and the purpose of the tool, which goes beyond the annotations without contradicting them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise: two sentences, with the first stating the primary function and the second giving explicit use cases. There is no unnecessary verbosity, and the most important information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a no-parameter, read-only statistics tool with an output schema and rich annotations, the description is fully complete. It communicates what data is returned and why to use it, covering both behavioral and selection aspects without redundancy.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. The schema description coverage is trivially 100%, and the description does not need to explain parameter behavior since there are none.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Get') and identifies the resource as 'database and search index statistics,' then lists example contents (counts of laws, norms, indexed vectors). This clearly distinguishes the tool from sibling tools like legal_search or legal_lookup, which serve different functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use the tool: 'Use for health checks or to understand the scope of the legal corpus.' This provides clear usage context. However, it does not explicitly mention alternatives or exclusions, so it falls short of the highest benchmark.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each legal tool targets a distinct operation: finding citing decisions, retrieving context, legislative history, stats, TOC, law lists, and exact lookups. The batch lookup is clearly a convenience wrapper for the single lookup, and the generic resource tools are unmistakably separate from the legal research functions.
All legal tools follow a consistent 'legal_' prefix plus verb_noun pattern (e.g., legal_find_citing_decisions, legal_get_context, legal_list_laws). The two generic MCP tools also follow verb_noun (list_resources, read_resource), maintaining overall consistency.
With 11 tools, the server is well-scoped for a comprehensive legal research domain. Each tool provides a distinct capability, and none feels redundant or excessive. The count is within the ideal 3-15 range.
The tool set covers the core legal research lifecycle: discovery (list_laws, search), lookup (lookup, lookup_batch), citation analysis (find_citing_decisions), structural navigation (get_toc, get_context), and legislative intent (get_materials). Minor gaps exist, such as the lack of coverage for certain state court decisions and non-German legislative materials, but these are explicitly documented and workarounds are provided.