AI Regulatory Watchdog: scrapes EBA/ESMA/BaFin/CERT-Bund for DORA updates. Returns alerts with affected articles and severity. Run daily via cron or on-demand.
| Name | Required | Description | Default |
|---|---|---|---|
| days_back | No | Check items from last N days (default: 7) |
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?
With no annotations provided, the description carries full responsibility for behavioral disclosure. It reveals that the tool scrapes external websites and returns alerts, which is useful. However, it does not mention potential side effects such as network rate limits, authentication requirements, or error handling, leaving some uncertainty about operational constraints.
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, consisting of two sentences that front-load the core function and then provide usage guidance. Every sentence adds value: the first explains what the tool does and returns, the second explains when to run it. No unnecessary filler or repetition of the tool name.
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 tool with only one optional parameter and no output schema, the description provides sufficient context: it names the data sources, the regulation monitored, the return value (alerts with articles and severity), and suggested usage. It could be slightly more complete by detailing the structure of the returned alerts, but the essentials are covered.
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 covers the single parameter 'days_back' with a clear description ('Check items from last N days (default: 7)'), giving 100% schema_description_coverage. The tool description does not add any additional parameter semantics beyond what the schema already provides, so the baseline score of 3 is appropriate.
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's function with a specific verb ('scrapes') and explicit resources (EBA/ESMA/BaFin/CERT-Bund) for DORA updates. It also specifies the output (alerts with affected articles and severity), which distinguishes it from sibling tools that focus on assessments, reporting, or contract analysis.
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 provides clear scheduling guidance ('Run daily via cron or on-demand'), which implies the tool is intended for periodic monitoring. It does not explicitly name alternatives or exclusions, but among the listed siblings, no other tool appears to perform regulatory scraping, making the intended use 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.