bafin_approve_send
Approve BaFin report for submission (4-eyes principle). Creates signed approval evidence.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| entity_id | No | ||
| report_id | No | ||
| approver_name | No | ||
| approver_role | No |
Approve BaFin report for submission (4-eyes principle). Creates signed approval evidence.
| Name | Required | Description | Default |
|---|---|---|---|
| entity_id | No | ||
| report_id | No | ||
| approver_name | No | ||
| approver_role | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
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 mentions 'Creates signed approval evidence,' indicating a write operation, but does not disclose whether the report is actually sent, what side effects occur, or any prerequisites. The behavior is under-specified for a mutation tool.
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 very concise, with two short clauses that both contribute to the core purpose. It is appropriately front-loaded, though it is perhaps too terse for the tool's complexity, which lowers it slightly from a perfect score.
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?
With four parameters, no output schema, and no annotations, the description is insufficient. It does not explain the approval workflow, what 'signed approval evidence' entails, or any consequences of approval. The tool's context in the broader BaFin reporting process is only hinted at via the 4-eyes principle.
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 input schema has four parameters with 0% description coverage, and the description adds no meaning to entity_id, report_id, approver_name, or approver_role. While some are inferable from names, the description fails to clarify required or optional usage, especially since no parameters are marked required.
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 clearly states the tool approves a BaFin report for submission using a 4-eyes principle, with a specific verb ('Approve') and resource ('BaFin report'). It does not explicitly contrast with sibling tools like bafin_report_draft, but the resource and action disambiguate it from other approval tools.
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 '4-eyes principle' provides clear context that this tool is part of a two-step approval workflow, implying use after drafting and before submission. It does not explicitly state when not to use it or name alternatives, but the context is sufficiently clear.
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.
Several tools overlap in purpose, particularly contract analysis (check_contract, contract_analyze, llm_clause_check) which could confuse agents. Assessment-related tools (readiness_check, assess_all, article_status) also have partially overlapping functionality. However, most tools have distinct resources and actions with detailed descriptions.
Tool names are consistently snake_case and mostly follow a verb_noun pattern (e.g., create_entity, generate_report, register_provider). Minor deviations like contract_analyze and llm_clause_check invert the verb-noun order, but the overall pattern is predictable.
50 tools is a very large surface for a single server, exceeding the threshold for 'too many' tools. While the DORA domain is broad, this breadth makes it challenging for agents to navigate and select the right tool efficiently.
The tool set covers the full DORA compliance lifecycle: entity onboarding, contract analysis, provider management, assessments, evidence, reporting, and incident workflows. Simulation and cross-regulation tools add depth. Minor missing CRUD operations (e.g., update/delete entity) but agents can work around.