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campaignstack_get_campaign_metrics

Read-onlyIdempotent

Get per-campaign metrics: latest snapshot plus a time-series for the requested date range. Includes leads contacted, responded, converted, failed, total leads, and average ICP match score. Use campaignstack_list_campaigns to find valid campaign IDs. The campaign must belong to your API key's workspace; metrics for campaigns in other workspaces are not returned. Use campaignstack_get_workspace_metrics for a workspace-level summary.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoNumber of calendar days to include in the range (default 30, max 90)
campaignIdYes

Schema Changelog

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

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses an important scoping behavior: campaigns outside the API key's workspace are not returned. It also clarifies that the call returns a snapshot and time-series, which is useful context given there is no output schema.

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?

Four purposeful sentences: the first identifies the core operation and output, the second enumerates returned metrics, the third covers ID lookup, and the fourth covers workspace scoping and the sibling alternative. No filler or redundant restatement.

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 two-parameter read-only tool with no output schema, the description is sufficient: it states what is returned, how to obtain valid IDs, the workspace constraint, and the sibling for aggregated metrics. An agent can select and invoke this tool without missing critical context.

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

Parameters4/5

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

The schema documents `days` with default/min/max, and the description provides meaning for the undocumented `campaignId` by saying valid IDs come from campaignstack_list_campaigns. It also frames `days` as the requested date range for the time-series, adding a little context beyond the schema.

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 per-campaign metrics, and specifies the exact output shape (latest snapshot plus a time-series). It clearly differentiates from sibling tools like campaignstack_get_workspace_metrics and campaignstack_get_campaign by naming the workspace-level alternative.

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?

It explicitly tells the agent to use campaignstack_list_campaigns to find valid campaign IDs before calling, and directs workspace-level needs to campaignstack_get_workspace_metrics. This gives clear when-to-use and when-not-to-use guidance for the most likely sibling alternatives.

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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