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Moe03

Convocore MCP Server

by Moe03

usage_read

Read-onlyIdempotent

Read usage, credits, and activity data for a workspace and per agent. Query credit balance, plan quotas, token consumption, and analytics over a defined time range.

Instructions

Read usage, credits and activity — everything the dashboard Usage and Analytics pages show — for the whole workspace and per agent. Read only. Time range: days back from now (default 30, max 400) or an explicit from / to (ISO date like 2026-10-01, or a full ISO datetime). overview: ONE call with credit balance, plan quota and limits, credits consumed in the range, tokens per model, the top agents (with names) and total activity (conversations, messages, calls, call minutes) — start here for "how is my usage". top_agents: agents ranked by credits consumed, with share of the total (topN up to 15). credits: current credit balance. quota: plan limits and current usage (conversations, messages, agents, KB…). workspace_usage: the Usage page — creditsCharged and creditsConsumed, input/output tokens per model, credits per agent with per-agent models, plus the chart list; add includeCharts for the chart data and includeLogs or logsPage for the credit usage log (what charged how much, and why). agent_usage (agentId): credits and tokens per model for one agent, plus call minutes. agent_analytics (agentId): that agent's analytics — loads, conversations, metrics, token usage, containment and human-handover stats. agents_analytics: activity totals for all agents (or agentIds), and per agent with includePerAgent (default true). clients_analytics: agency only — usage, leads and funnel per whitelabel client and per agent (clientIds to narrow). email_analytics (agentId): email channel analytics for one agent. WhatsApp and SMS analytics are in whatsapp_numbers_read / sms_numbers_read, voice calls in call_logs_read, knowledge-base quota in get_kb_quota.

Input Schema

NameRequiredDescriptionDefault
toNoRange end: ISO date (2026-10-31 = end of that UTC day) or datetime. Default now.
daysNoLook back this many days from now (1-400, default 30). Ignored when from is set.
fromNoRange start: ISO date (2026-10-01 = start of that UTC day) or datetime.
modeNocompact (default) = long text is cut and bulky blobs are replaced by a size marker. full = complete response.
topNNoFor overview / top_agents: how many top agents to return (1-15; default 8 for overview, 15 for top_agents).
actionYes
agentIdNoFor agent_usage / agent_analytics / email_analytics: the agent (list_agents or search_agents).
agentIdsNoFor agents_analytics: only these agents (max 500). Default all agents in the workspace.
logsPageNoFor workspace_usage: usage log page number (implies includeLogs).
clientIdsNoFor clients_analytics: only these whitelabel clients (clients_read list).
includeLogsNoFor workspace_usage: include the credit usage log (first page).
includeChartsNoFor workspace_usage: include the chart data (credits over time, usage by feature / agent, tokens).
includePerAgentNoFor agents_analytics: also return one row per agent (default true).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.6.17

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and open-world, so the bar is lower; the description still adds useful context — workspace-wide vs per-agent scope, the 400-day cap, and agency-only gating on clients_analytics. It restates 'Read only' (redundant with readOnlyHint) and says nothing about rate limits or response size beyond what the schema's mode already covers.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with purpose, then time-range rules, then an action-by-action inventory in the same order as the enum — dense and largely waste-free. It is a single wall of prose rather than listed items, which makes a ten-action dispatcher harder to scan than it needs to be.

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?

With ten actions, 13 parameters and no output schema, the description carries the return-value burden and does so by naming the actual metrics per action plus availability constraints (agency only) and the pointers for analytics handled elsewhere. Practically everything needed to select an action and call it correctly is present.

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

Parameters5/5

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

Schema coverage is 92% so the baseline is 3, but the action enum has no description in the schema at all and the description supplies all of it — defining the meaning and return shape of each of the ten values. It also documents cross-parameter interactions the schema only hints at (logsPage implies includeLogs, includePerAgent defaults true, topN default differs by action).

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 ('Read usage, credits and activity') and then enumerates all ten actions with concrete payload fields ('creditsCharged and creditsConsumed, input/output tokens per model...'). It also explicitly names the boundaries against siblings (WhatsApp/SMS analytics live in whatsapp_numbers_read / sms_numbers_read, voice in call_logs_read, KB quota in get_kb_quota), so an agent can separate it from neighbours without opening a schema.

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?

Strong routing guidance: 'overview ... start here for "how is my usage"' tells the agent the default entry point, and out-of-scope data is redirected to four named sibling tools. It does not, however, distinguish this tool's agent_usage action from the sibling get_agent_usage / get_agent_usage_bulk, which is the one obvious overlap left unexplained.

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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