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campaignstack_list_companies

Read-onlyIdempotent

List companies linked to a workspace. Returns company records with workspace metadata (tags, notes, addedAt). Supports cursor-based pagination. Use the returned companyId values with campaignstack_get_company.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
cursorNo
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

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. Beyond that, the description adds useful behavioral detail: it returns company records with workspace metadata (tags, notes, addedAt) and supports cursor-based pagination. It does not mention edge behavior such as omitted workspaceId, but that nuance is present in the schema, so the added disclosure is solid.

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?

Three sentences, each earning its place: purpose, return contents, pagination and next-step usage. There is no filler or redundancy, and the most important scoping statement 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?

For a read-only list tool with no required parameters and no output schema, the description covers the essential context: what is listed, what fields are returned, how pagination works, and how to consume the returned companyIds. The schema fills in the remaining parameter constraints, so an agent has enough to invoke the tool correctly.

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 only 33%; only workspaceId has an inline description. The description adds meaning to cursor via 'Supports cursor-based pagination' and clarifies workspaceId via 'linked to a workspace,' but it never explains the limit parameter beyond what the schema's name and constraints imply. This leaves a small but real semantic gap.

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 and resource: 'List companies linked to a workspace.' It also distinguishes the tool from the closely related campaignstack_get_company by directing the agent to feed returned companyId values into that tool. This makes the purpose unambiguous without needing to inspect sibling tools.

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 establishes when to use this tool: to enumerate companies linked to a workspace, with cursor-based pagination. It also gives a concrete downstream usage pointer to campaignstack_get_company. It does not explicitly state when not to use it or name alternatives, but the context is clear enough for an agent to route correctly.

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