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campaignstack_update_lead_list

Idempotent

Update a lead list: rename it and/or update its query predicate. Setting a query converts the list to query type. Use campaignstack_list_lead_lists to find valid lead list IDs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
queryNo
leadListIdYes

Schema Changelog

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

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already indicate idempotentHint=true and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral detail beyond annotations: setting a query converts the list to query type. This is useful state-changing context that helps the agent predict the tool's effect.

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?

Two sentences, front-loaded with the core action and parameters, with no filler. The behavioral note about query conversion and the reference to the ID-lookup sibling are both essential and compactly placed.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description gives the essential operation, the conversion behavior, and where to find IDs, which is adequate for a simple update. However, the query object is a nested structure with required include/exclude/filter/logic fields and zero schema-level descriptions; the description does not sufficiently compensate, so an agent may not know how to construct a valid query or what each field means.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate, but it only maps name to 'rename' and query to 'query predicate'. It does not explain the meaning of the nested query fields include, exclude, filter, logic, or excludedLeadIds, nor does it clarify the leadListId parameter beyond pointing to the list tool. This leaves the most complex part of the input schema under-explained.

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?

States a specific verb and resource: 'Update a lead list', then enumerates the two concrete modification actions: rename and update query predicate. This clearly distinguishes it from related tools like create_lead_list and remove_lead_list, and the 'converts the list to query type' note clarifies the semantic effect of the query parameter.

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?

Covers prerequisite context by directing the agent to campaignstack_list_lead_lists for valid lead list IDs, which is essential for correct invocation. It does not explicitly state when to prefer this over create_query_lead_list or create_lead_list, but the 'update' framing and 'converts to query type' note give clear usage context without exclusions.

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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