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Get Integration Guide

get_integration_guide
Read-only

Get a step-by-step integration guide for connecting your LLM application to Converra. Describe your tech stack (e.g., 'Next.js with Vercel AI SDK', 'Python FastAPI with OpenAI', 'Voice agent with OpenAI Realtime API') and get the exact code, packages, and configuration needed.

Input Schema

NameRequiredDescriptionDefault
stackYesYour LLM tech stack — e.g., 'Vercel AI SDK with streamText', 'OpenAI Python SDK', 'Anthropic + LangChain', 'OpenAI Realtime/Voice API', 'webhook-based — I can POST runs when they end'
runtimeNo(Optional) Runtime environment — e.g., 'Node.js', 'Edge/serverless', 'Lambda', 'Vercel Functions'
agentNameNo(Optional) Name for your agent in Converra — e.g., 'Support Bot', 'Voice Receptionist'
frameworkNo(Optional) Web framework — e.g., 'Next.js', 'Express', 'FastAPI', 'Flask'

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=false, so the safety profile is covered. The description usefully adds that output is a generated, stack-tailored guide containing exact code, packages, and configuration. It does not disclose anything about how the guide is generated or its size/format, keeping it short of a 5.

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?

Two sentences, front-loaded with the purpose and then the input/output contract; no filler and every clause earns its place.

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

Completeness4/5

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

With no output schema, the description compensates by naming the deliverables (code, packages, configuration). Combined with full parameter coverage and a read-only annotation profile, an agent has enough to call it, though it could say more about output shape.

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% and each parameter already carries rich examples, so the schema does the heavy lifting. The description only restates that 'stack' should describe your tech stack, adding no syntax or constraint detail beyond the schema.

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?

States a specific verb+resource ('Get a step-by-step integration guide') scoped to connecting an LLM app to Converra, and it is readily distinguishable from siblings like verify_integration and list_integrations. An agent knows exactly what this returns.

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

Usage Guidelines3/5

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

It implies the usage pattern (describe your stack, receive tailored code/packages/config) but never states when to reach for this versus verify_integration or list_integrations, nor any prerequisites or exclusions. Usage is inferable but not guided.

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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