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DevFlow MCP Server

DevFlow MCP Server

by FaerberK
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

knowledge_backfill_request

Request a knowledge backfill run to receive completed flows, existing ADRs, and structured instructions for classifying and proposing knowledge drafts for a project.

Instructions

Prepare a knowledge backfill run for a project. Returns done-flows + existing ADRs + structured instructions that YOU (Claude) must follow to classify and propose knowledge drafts.

This is the MCP-first alternative to the keyword heuristic. Workflow:

  1. Call this tool → receive flows, existing ADRs, and the ruleset

  2. Read the flows, decide which qualify as ADR / Pattern / Runbook / Lessons-Learned

  3. Skip anything already covered by existing ADRs

  4. Group similar flows into ONE draft with multiple sourceFlowIds

  5. For each draft: call knowledge_draft_create with the payload

Be conservative. Only propose drafts you are confident about. Better few high-quality drafts than many noisy ones.

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": "knowledge_backfill_request"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMax flows to include (default 50, max 100)
projectIdNoProject ID (defaults to linked project)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv4.35.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations provided, the description does not explicitly state non-destructive behavior or authorization needs. However, it clearly describes the tool as a preparatory read operation and outlines the expected flow, which provides substantial transparency.

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

Conciseness5/5

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

The description is well-structured with a clear purpose statement and bulleted workflow steps. Every sentence adds value, and it is front-loaded with the core purpose. It is concisely written despite its length.

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

Completeness5/5

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

Despite lacking annotations and an output schema, the description fully compensates by explaining the tool's role, its output components, and the subsequent steps the agent must take. It is complete for the tool's complexity.

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 100% for two parameters, with the description adding no additional semantic information beyond what the input schema already provides. Baseline score of 3 is appropriate.

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 clearly states that the tool prepares a knowledge backfill run for a project, contrasting with the keyword heuristic and listing the exact output (done-flows, existing ADRs, instructions). It effectively differentiates from sibling tools like knowledge_harvest by specifying its role as an MCP-first alternative.

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

Usage Guidelines5/5

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

The description provides an explicit 5-step workflow detailing when to call the tool, how to process its output, and when to invoke knowledge_draft_create. It includes cautionary advice ('Be conservative'), effectively guiding the agent on usage.

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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