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campaignstack_optimize_split_node

Idempotent

Run split-node auto-optimization immediately for a flow:split workflow node, instead of waiting for the daily cron. Re-weights branches via Thompson Sampling on downstream positive exits (replies, acceptances, meetings) and may kill severe underperformers (weight 0). Skips (with an explanatory status) when any branch is below the node's minimum lead volume or the weight change is insignificant. Returns a status string. Use campaignstack_get_workflow_stats to find split node IDs, and campaignstack_list_split_optimization_logs to inspect past optimizations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nodeIdYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the annotations, the description discloses Thompson Sampling re-weighting, downstream positive-exit criteria, the possibility of killing underperformers via weight 0, skip conditions, and the return type. This gives the agent a clear picture of side effects and edge-case behavior; no contradiction with the annotations is evident.

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?

Every sentence contributes distinct information: purpose, algorithm, skip behavior, return value, and discovery/logging pointers. The purpose is front-loaded and there is no filler or repetition.

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?

For a one-parameter mutation tool with annotations and no output schema, the description covers the operation, algorithmic behavior, side effects, skip cases, return value, and how to obtain valid inputs. Nothing critical is missing for an agent to select and invoke the tool correctly.

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

Parameters4/5

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

The schema provides only the name 'nodeId' with no description, so the description's pointer to campaignstack_get_workflow_stats for finding split node IDs adds useful semantic value. It does not fully spell out the parameter format, but the single parameter is self-explanatory and the 'flow:split workflow node' context disambiguates it.

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 precise action ('Run split-node auto-optimization immediately'), a specific resource ('flow:split workflow node'), and contrasts it with waiting for the daily cron. It also names related sibling tools for finding node IDs and inspecting logs, making it clearly distinguishable.

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?

It explicitly frames the tool as the immediate alternative to the daily cron, and explains when it may skip execution (insufficient lead volume or insignificant weight change). It also directs the agent to campaignstack_get_workflow_stats for node ID discovery and campaignstack_list_split_optimization_logs for past optimizations.

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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TDQS

A3.7/5.0
Disambiguation3/5

The set is enormous and generally well-differentiated through detailed cross-referenced descriptions, but several clusters blur together: archive/delete/remove have inconsistent permanence semantics (delete_campaign vs remove_signal_watch vs archive_campaign), create_connection_watch_agent explicitly overlaps with set_account_watcher, and the parallel draft-checkup and playbook-proposal flows (run_draft_checkup/get_draft_checkup/accept_draft_checkup vs propose_playbook_change/get_playbook_proposal/decide_playbook_proposal) present near-identical decision pipelines.

Naming Consistency4/5

Nearly every tool follows the campaignstack_<verb>_<noun> convention with disciplined get/list pairing and consistent verb choices (create/update/delete/pause/resume). Minor deviations like campaignstack_priority_enrich (adverb+verb) and campaignstack_whoami break the strict verb_noun pattern but are isolated and do not hinder navigation.

Tool Count1/5

223 tools is an extreme surface for any MCP server. Even though each tool maps to a distinct API operation and the underlying platform is broad, the scale far exceeds the 50+ threshold for an extreme mismatch and will overwhelm agents with selection overhead.

Completeness5/5

The surface is exhaustive for the LinkedIn outreach domain: full campaign/workflow/lead-list lifecycles, ICP and persona management, content scheduling and approvals, inbox and messaging, enrichment and integrations, signal watches and exclusions, review queues, playbook versioning, workspace admin, billing, and notifications. Minor gaps like a missing delete_lead or delete_company are explained by shared-data semantics, so no critical dead ends remain.

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