Get paginated audit logs for the current organization.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | ||
| limit | No | ||
| action | No | ||
| result | No | ||
| actor_email | No | ||
| request_hash | No | ||
| resource_type | No | ||
| manifest_version | No | ||
| policy_snapshot_id | No | ||
| schema_snapshot_id | 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?
The description only mentions pagination, ignoring behavioral traits like filtering parameters (action, result, etc.), rate limits, or what happens on empty results. With no annotations, the description carries full burden but provides insufficient detail.
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 a single sentence, concise but lacks necessary detail. It is not overly verbose, but it could be expanded to include filtering and behavior without sacrificing conciseness.
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?
Given 10 parameters, no output schema, and no annotations, the description is insufficient. It does not explain pagination mechanics, filter usage, or return value structure, leaving many gaps for the agent.
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 0%, so the description must clarify parameter meanings, but it only implies pagination via 'paginated'. Parameter names (e.g., action, result) are self-explanatory to some extent, but no guidance on valid values or combinations is provided.
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 retrieves audit logs for the current organization with pagination. However, it does not differentiate from sibling tools like get_audit_log_artifacts or other list tools, which would justify a higher score.
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?
No guidance on when to use this tool versus alternatives such as get_audit_log_artifacts or list_agent_runs. The description omits context like organization scope or prerequisites.
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.
Most tools target distinct resources or actions, but there is some overlap (e.g., run_repository_fix vs run_repository_pipeline vs simulate_repository) that could cause confusion. Overall, descriptions help differentiate.
Tool names are primarily snake_case with a verb_noun pattern, but there are inconsistencies (e.g., single-word verbs like 'simulate', 'tokenize', and mixed prefixes like 'preview_', 'product_'). The pattern is readable but not uniform.
With 140 tools, the server is extremely over-scoped for typical MCP usage. This overwhelms agents and suggests poor separation of concerns, likely violating the principle of minimal tool surfaces.
The tool set covers a wide range of functionalities including data onboarding, simulation, decisions, repository management, and admin operations. Minor gaps exist (e.g., no update_agent_run), but core workflows are well-supported.
