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deep-agentic-core-mcp

deep-agentic-core-mcp

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by DeepAgentLabs
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Python
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Analyze Workflow

lens.analyze_workflow

Analyze an AgenticLens-compatible workflow artifact, validate recommendations with provenance (digest, step, evidence, producer), and summarize how many resolve.

Instructions

Analyze an AgenticLens-compatible workflow artifact. Each recommendation carries a provenance block (artifact digest, workflow step, evidence span IDs, producer) and the response summarizes how many resolve.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "artifact": "<artifact>"
    },
    "name": "lens.analyze_workflow"
  }
}

Input Schema

NameRequiredDescriptionDefault
artifactYes
session_idNoSession to read/write shared state under. Defaults to 'default'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

B3.2/5.0
Behavior3/5

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

Annotations only declare readOnlyHint=false, and the description never discloses the associated side effect that the schema hints at (session_id 'read/write shared state'). It does add real value by describing the return shape (a provenance block with digest, step, evidence spans, producer, and a resolution count), which matters because no output schema exists. Net: useful output context, but the mutation/shared-state behavior is left undisclosed.

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?

Two sentences, purpose front-loaded, no filler. The second sentence earns its place by describing the provenance/return contract that no output schema supplies, though the phrasing is dense and jargon-heavy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a write-capable tool with a nested, undocumented artifact parameter and no output schema, the definition covers the return format but omits the artifact's shape, the meaning of 'recommendation,' and what resolution actually does. Adequate but with clear gaps.

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

Parameters3/5

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

Schema coverage is 50%: session_id is documented in the schema, while artifact is an undescribed nested object. The description partially compensates by clarifying that artifact is an 'AgenticLens-compatible workflow artifact,' but gives no structure, required fields, or example. Baseline 3 fits a half-documented two-param schema.

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?

States a specific verb and resource: 'Analyze an AgenticLens-compatible workflow artifact.' This distinguishes it from generic lens tools by naming the artifact type, but it never differentiates itself from near siblings like lens.score_analysis, lens.diagnose_run, or lens.audit_report, which also analyze artifacts. Clear but sibling-blind.

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?

There is no when-to-use or when-not-to-use guidance, no prerequisites, and no routing to alternatives. Absent a comparison against lens.diagnose_run or spec.validate_artifact, an agent must guess which analysis tool applies to a given artifact.

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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