Skip to main content
Glama
DeepAgentLabs

deep-agentic-core-mcp

Official

Audit Report

lens.audit_report
Read-only

Retrieve case-by-case evaluation details from an audit trail to understand each decision's outcome. Diagnose failures by inspecting granular assessment data.

Instructions

Return case-by-case evaluation detail for an audit trail.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
reportYes
session_idNoSession to read/write shared state under. Defaults to 'default'.
include_htmlNo
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The readOnlyHint annotation already communicates a non-mutating operation, and the description's 'Return' is consistent with that. However, the description adds little beyond the annotation, offering no detail about potential size, pagination, performance, or preprocessing behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single efficient sentence with the core verb and object front-loaded. It is concise and scannable, though it may be too terse to provide full context.

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?

With no output schema, nested input objects, and low parameter documentation, the description leaves significant gaps. It does not define the expected 'report' input, the semantics of 'include_html', or the shape of the returned case-by-case details, so an agent would struggle to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 33%, with 'report' and 'include_html' lacking descriptions. The description does not explain what the 'report' object should contain, how 'include_html' affects output, or what 'audit trail' means, so it fails to compensate for the schema gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action ('Return') and a resource ('case-by-case evaluation detail for an audit trail'), which conveys more than the tool name alone. It hints at a report-level detail view, but does not explicitly distinguish itself from siblings like lens.report_summary or lens.compare_runs.

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 about when to use this tool versus sibling tools such as lens.report_summary, lens.analyze_workflow, or lens.slo_summary. The phrase 'for an audit trail' implies some context, but there are no explicit conditions, exclusions, or alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/DeepAgentLabs/mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server