Get all topics assigned to a campaign along with their weight (0–1). Topics with weight > 0.8 have active LinkedIn search watch automations. Returns topic metadata (id, name, displayName, category) and weight for each entry. If the campaign is not found, use campaignstack_list_campaigns to find valid campaign IDs.
| Name | Required | Description | Default |
|---|---|---|---|
| campaignId | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds useful behavioral context: weight range (0–1), the meaning of weights above 0.8, the shape of returned entries, and guidance for invalid campaign IDs. This goes beyond the annotations without contradicting them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three efficient sentences: primary purpose, key weighted-topic semantics, and an error-recovery pointer. No filler or redundant restatement of the tool name, and the most important information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only tool with one parameter and no output schema, the description is complete. It tells the agent what the tool does, what data it returns, what a notable weight threshold means, and how to recover if the campaign ID is invalid.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description carries the burden. It does not explicitly define campaignId, but the repeated references to 'campaign' and the fallback to campaignstack_list_campaigns imply its role. For a single, self-explanatory parameter this is adequate, though a direct statement like 'campaignId identifies the target campaign' would have been clearer.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get all topics assigned to a campaign') and the resource ('topics ... along with their weight (0–1)'). It also details the returned metadata (id, name, displayName, category) and weight, making it distinguishable from related campaign getters such as get_campaign_weights or list_search_topics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides concrete guidance for a common failure scenario: if the campaign is not found, use campaignstack_list_campaigns to find valid campaign IDs. It does not explicitly enumerate when to prefer this tool over sibling getters, but the context is clear enough for typical use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.
