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campaignstack_get_contact_finder_request

Read-onlyIdempotent

Get one built-in Contact Finder request by id. Returns its status (queued, linked, submitted, completed, failed), what was wanted (wantEmail, wantPhone), creditsReserved at enqueue, creditsCharged once settled (reserved minus refunds, i.e. only data that was found), the result (emailFound, emailStatus, phoneFound, emailWritten, phoneWritten) and errorCode on failure. Poll it after campaignstack_enrich_lead_contact_info returned status pending with provider bettercontact; a request usually completes within minutes and never later than about an hour. Found data is already written on the lead (campaignstack_get_lead), this tool never returns the email or phone itself.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestIdYesRequest id returned by campaignstack_enrich_lead_contact_info (provider bettercontact).
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: -"Workspace ID (defaults to the bound workspace)"New value: +"Workspace ID (required for user keys; workspace keys are bound)"
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already establish readOnlyHint, idempotentHint, and destructiveHint, but the description adds substantial behavioral context beyond them: the status lifecycle, credit reservation and refund semantics, result fields, errorCode on failure, polling expectation, and the side effect that found data is already written to the lead. No contradiction exists between description and 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?

The description is dense but every sentence earns its place: it states the purpose and return fields first, then the polling trigger and timing, then the crucial caveat about data already being written elsewhere. There is no filler or redundant repetition of schema or annotation information.

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 simple two-parameter read-only polling tool with no output schema, the description fully compensates: it names all key response categories, explains settlement semantics, discloses the failure field, gives timing boundaries, and tells the agent where to find the actual data. Nothing critical is missing.

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 schema already documents requestId and workspaceId adequately. The description reinforces that requestId comes from campaignstack_enrich_lead_contact_info, but this is also stated in the schema. It adds no new parameter-level meaning, so the baseline of 3 applies.

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: 'Get one built-in Contact Finder request by id.' It clearly distinguishes this single-item retrieval tool from the sibling list_contact_finder_requests by emphasizing 'one' and 'by id.' It further enumerates the meaningful response fields, leaving no ambiguity about what the tool does.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use it: 'Poll it after campaignstack_enrich_lead_contact_info returned status pending with provider bettercontact.' It also gives a practical expectation for completion time and warns that the tool never returns the email or phone itself since found data is already written on the lead, guiding the agent toward campaignstack_get_lead for that data.

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