deep-agentic-core-mcp

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Compare Runs

lens.compare_runs

Compare baseline and candidate trace runs to identify regressions. Optionally reuses session-stored runs for quick analysis.

Instructions

Compare baseline and candidate trace runs for regressions. 'baseline'/'candidate' may be omitted to reuse the session's stored runs.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "lens.compare_runs"
  }
}

Input Schema

Name	Required	Description
`baseline`	No
`candidate`	No
`session_id`	No	Session to read/write shared state under. Defaults to 'default'.
`regression_threshold`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Aug 30, 2026

TDQS

A3.6/5.0

Behavior3/5

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

Annotations only indicate readOnlyHint=false, leaving room for side effects. The description adds useful behavioral context by mentioning that baseline/candidate can reuse the session's stored runs, but it does not disclose whether the comparison writes results back to session state or what else may be modified.

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 concise and front-loaded, with each sentence adding distinct information: the core purpose and the optionality/reuse behavior. It could be somewhat richer given the sparse schema, but there is no fluff or redundancy.

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?

For a tool with 4 parameters, low schema coverage, no output schema, and a non-read-only annotation, this description is not sufficient for an agent to invoke it confidently. It omits what trace runs look like, how regression_threshold behaves, and what the tool returns.

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 25% and the description only partially compensates. It clarifies that baseline/candidate can be omitted to reuse stored runs, but the contents of the array item objects and the meaning of regression_threshold are left entirely undocumented.

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 states a specific action ('Compare baseline and candidate trace runs') and a clear goal ('for regressions'), going beyond the generic title. This clearly differentiates it from sibling analysis/reporting tools like lens.analyze_workflow and lens.report_summary.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear invocation context: use this tool to detect regressions between baseline and candidate runs. It also explains when parameters may be omitted by referencing reused session state, though it does not explicitly name alternatives or exclusion conditions.

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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