whatif_stale
Simulate stale evidence: what happens if a check stays stale for N days.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Days stale (default 30) | |
| check_id | No | Check ID | |
| entity_id | No | Entity ID |
ampel
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
B3.2/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It suggests a non-destructive 'simulate' operation but does not disclose whether it mutates state, requires permissions, returns a report, or has side effects. This is insufficient for a tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that states the action and the scenario without any filler. Every word contributes meaning, making it highly concise and efficiently structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has three optional parameters and no output schema, yet the description does not explain what the simulation returns, how the identifiers are used, or what 'stale' means. It leaves significant gaps that are not compensated by annotations or output schema, so the overall context is incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, with field descriptions already providing baseline semantics. The description adds context by mapping 'N days' to the days parameter and tying the scenario to 'stale evidence', but it does not explain the relationship between check_id and entity_id or their optionality in detail.
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?
The description uses the specific verb 'Simulate' with the resource 'stale evidence' and states the scenario 'if a check stays stale for N days'. This clearly defines the tool's purpose and distinguishes it from siblings like whatif_provider, which focuses on provider scenarios.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives such as freshness_check or whatif_provider. The simulation intent is implied, but there are no explicit context cues, exclusions, or recommendations, leaving the agent to infer suitability.
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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TDQS
B3.1/5.0
Disambiguation3/5
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.
Naming Consistency4/5
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.
Tool Count2/5
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.
Completeness4/5
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.