Live Azure AD integration: MFA registration %, risky users, conditional access policies. DORA Art. 9 evidence. Requires Azure AD config in integrations_config.json.
| Name | Required | Description | Default |
|---|---|---|---|
| force_refresh | No | Force fresh API call (default true) |
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, the description must carry the burden of behavioral disclosure. It discloses the 'Live' nature (real-time API call) and the config requirement (setup/auth context). However, it does not explicitly state read-only behavior, potential side effects, or any rate limits. This is a partial disclosure, hence a 3.
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 only two sentences, front-loaded with the core function, and every phrase adds value (data points, regulatory purpose, config prerequisite). No fluff or redundancy.
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 simple tool with one parameter and no output schema, the description is quite complete: it lists the data retrieved, the regulatory context (DORA Art. 9), and the setup requirement. It could be slightly more explicit about the exact return format, but the listed data points largely cover that. A 4 is appropriate.
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?
Schema coverage is 100% for the single parameter (force_refresh), which the schema already describes as 'Force fresh API call (default true)'. The description adds no parameter-specific meaning, so the baseline 3 applies.
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 specifies what the tool does: it checks Azure AD integration and lists concrete data points (MFA registration %, risky users, conditional access policies). This distinguishes it from sibling tools focused on contracts, reports, and assessments. The verb is implied by the tool name 'azure_ad_check' and reinforced by 'Live integration'.
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?
Provides explicit usage context: 'DORA Art. 9 evidence' tells when this tool should be used, and the config requirement gives a prerequisite. However, it does not explicitly mention when not to use it or point to alternative tools, so it's clear but not fully differentiated.
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.