ld_architect

Architecte formation & développement — Gapup agent-payable C-suite expertise (CHRO). Returns a structured, audited deliverable. Reference case: Pennylane (180 FTE) — Catalogue 8 formations · 3 parcours individuels · ROI €480k · Payback 7 mois. Inputs are validated server-side — send the documente...

SERVERGapup Mcp SOURCEhttps://mcp.gapup.io/mcp
Medium RISK CLASS
Category Write
Parameters 54 required
Recommended Rate-limitedsee the rule below
Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/io-github-getgapup-gapup-mcp/ld-architect.md

What ld_architect does on Gapup Mcp

AI agents use ld_architect to create or update resources in Gapup Mcp, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Gapup Mcp environment.

ParameterTypeRequiredDescription
team object Yes
async boolean If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client ti
budget object Yes
company object Yes
learningNeeds array Yes

Parameters from the server's own tool schema.

Why ld_architect is rated Medium

The tool generates and returns structured organizational development deliverables (training catalogues, individual development paths, ROI projections). This is data creation/modification rather than mere retrieval (Read), but not destructive or financial in nature.

From the tool's definition Tool returns 'structured, audited deliverable' that involves 'formation & développement' planning with reference to organizational training programs (formations, parcours). This creates new training/development plans and documentation artifacts.

Risk signalsHigh parameter count (15 properties)

Questions about ld_architect

What does the ld_architect tool do? +

Architecte formation & développement — Gapup agent-payable C-suite expertise (CHRO). Returns a structured, audited deliverable. Reference case: Pennylane (180 FTE) — Catalogue 8 formations · 3 parcours individuels · ROI €480k · Payback 7 mois. Inputs are validated server-side — send the documented case fields. It is categorised as a Write tool in the Gapup Mcp MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

What parameters does ld_architect accept? +

ld_architect accepts 5 parameters: team, async, budget, company, learningNeeds. Required: team, budget, company, learningNeeds. The full parameter table on this page comes from the server's own tool schema.

How do I enforce a policy on ld_architect? +

Register the Gapup MCP server in PolicyLayer and add a rule for ld_architect: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Gapup Mcp. Nothing to install.

What risk level is ld_architect? +

ld_architect is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit ld_architect? +

Yes. Add a rate_limit block to the ld_architect rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.

How do I block ld_architect completely? +

Set action: deny in the PolicyLayer policy for ld_architect. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.

What MCP server provides ld_architect? +

ld_architect is provided by the Gapup MCP server (https://mcp.gapup.io/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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