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campaignstack_build_search_url

Read-onlyIdempotent

Build a LinkedIn People search URL from ICP (Ideal Customer Profile) criteria. Input ICP fields like titles, industries, locations, seniorities, company sizes, and keywords. Returns a ready-to-use LinkedIn search URL. Use campaignstack_list_icps to get ICP criteria for a campaign, then pass them here to generate a search URL. The resulting URL can be used with campaignstack_queue_leads to import search results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titlesNo
keywordsNo
locationsNo
industriesNo
senioritiesNo
companySizesNo

Schema Changelog

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

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior, and the description adds that this returns a 'ready-to-use LinkedIn search URL' rather than executing a search. The build-and-return framing makes the pure, side-effect-free nature clear without contradicting the annotations.

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 short sentences front-load the purpose, then describe inputs, output, and surrounding workflow. Every sentence adds useful information and there is no filler.

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?

For a pure URL-builder with safe annotations, the description explains the key pipeline context: where criteria come from, what is returned, and how to use the result. It is missing minor details like whether criteria are AND/OR combined and that all parameters are optional, but the schema and sibling workflow cover much of that.

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 0%, so the description carries the parameter documentation burden. It does list all six ICP field groups ('titles, industries, locations, seniorities, company sizes, and keywords'), but it adds no formatting or semantics beyond the schema's property names, such as the structure of the location objects or constraints.

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: 'Build a LinkedIn People search URL from ICP criteria', then enumerates the input fields and the output. This clearly separates it from search, import, and creation siblings like campaignstack_search_leads and campaignstack_queue_leads.

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?

It gives an explicit workflow: call campaignstack_list_icps to fetch ICP criteria, pass them here, then feed the URL to campaignstack_queue_leads. It does not explicitly state when not to use it or name alternatives, so it stops short of a 5.

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