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campaignstack_get_playbook

Read-onlyIdempotent

Get the workspace's playbook as playbookSections, one field per section: identity (sent on every message), voice (sent on every message), boundaries (sent on every message), angles (sent on messages we send first), objections (sent on replies, after they have written back). Also returns offerContext, the factual company/offer grounding injected into every AI craft regardless of playbook resolution, and capabilities, the description of what the system behind the workspace can detect and do that is injected into reply crafts only. All three are editable via campaignstack_update_workspace. Returns null if the workspace does not exist; an unwritten playbook comes back as an empty object. Use campaignstack_regenerate_playbook to create or refresh it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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

A4.5/5.0
Behavior5/5

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

Beyond the readOnly/idempotent/non-destructive annotations, the description discloses meaningful return behavior: null for nonexistent workspaces, empty object for unwritten playbooks, and the precise semantic role of offerContext and capabilities in message crafting. This gives the agent the behavioral expectations it needs without an output schema.

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 dense but every sentence carries operationally relevant information: return shape, field semantics, edit path, edge cases, and regeneration alternative. The main verb and resource appear first, so the core purpose is front-loaded.

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?

With no output schema, the description carries the full burden of explaining return values, and it does so comprehensively: each section, the special offerContext/capabilities behavior, null handling, empty-object handling, and how to modify or create the playbook. Nothing material is missing for a single-parameter read tool.

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 single workspaceId parameter is already documented in the schema. The description adds little about parameter mechanics beyond referring to 'the workspace', which aligns with the schema's own wording. Baseline 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 opens with a specific verb-resource pair, 'Get the workspace's playbook', and enumerates the exact fields returned (playbookSections, offerContext, capabilities) with their injection contexts. It also distinguishes itself from related operations by naming campaignstack_update_workspace and campaignstack_regenerate_playbook as the edit/create counterparts.

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 clearly states what this tool returns and gives explicit pointer to campaignstack_regenerate_playbook for creating/refreshing and campaignstack_update_workspace for editing. It does not enumerate when to choose this over closer read siblings like campaignstack_get_workspace_voice or campaignstack_get_platform_capabilities, so it stops short of a full exclusion list.

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.

Resources