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campaignstack_propose_playbook_change

Ask the playbook assistant for a change to the workspace's craft data (playbook sections, outreach intent details, offer context) and get a proposal back. This tool NEVER writes: it returns an assistant message and, when a change fits, a proposalId whose current/proposed text you read with campaignstack_get_playbook_proposal and apply with campaignstack_decide_playbook_proposal. A question gets an answer and no proposal. A new proposal replaces the workspace's pending one. Runs an LLM call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestYesWhat should change about how the workspace writes, in plain words, e.g. 'stop mentioning pricing in openers' or 'sound less corporate'
workspaceIdNoWorkspace ID (required for user keys; workspace keys are bound)

Schema Changelog

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

  1. Changed1 schema field changed
    • changedInput schema / properties / workspaceId / description
      Previous value: -"Defaults to the API key's workspace"New value: +"Workspace ID (required for user keys; workspace keys are bound)"
  2. First observed

TDQS

A3.6/5.0
Behavior1/5

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

The description explicitly claims 'This tool NEVER writes', but the annotations declare readOnlyHint=false. This is a direct contradiction with the annotation. Additionally, the description itself says 'A new proposal replaces the workspace's pending one,' which implies a write to workspace state. Because the description contradicts the annotations, it fails behavioral 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 compact and information-dense. It front-loads the core purpose, then adds the critical non-write caveat, the proposal flow with companion tools, and behavior for questions vs. changes. Every sentence contributes useful operational context without padding.

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 present, the description adequately covers return behavior: an assistant message, a proposalId when a change fits, no proposal for questions, and replacement of pending proposals. It also notes the LLM call. Minor gaps like error cases or response formatting keep it from a 5, but the essentials are present.

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 description coverage is 100%, so the schema already documents both parameters well. The description adds some value by framing the request as plain-language instructions and by explaining that workspaceId is key-bound for workspace keys, but it does not meaningfully extend beyond what the schema already provides.

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 clear, specific action: asking the playbook assistant for a change to craft data and receiving a proposal. It distinguishes itself from related sibling tools by naming campaignstack_get_playbook_proposal and campaignstack_decide_playbook_proposal as the follow-up tools for reading and applying the proposal.

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

Usage Guidelines4/5

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

The description gives concrete usage context: ask for a change, get a proposal; questions get answers without proposals; new proposals replace pending ones. It also explains the workflow with companion tools. It does not explicitly state when to use an alternative instead, but the boundary is clear enough for an agent.

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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