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thenavidm

substack-mcp

by thenavidm
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

Get the dashboard summary

get_dashboard_summary
Read-onlyIdempotent

Retrieve key newsletter metrics—subscriber counts, paid subscribers, ARR, and recent activity—to answer how a publication is performing over a chosen period.

Instructions

The headline numbers from the publishing dashboard: subscribers at the start and end of the window, paid subscribers, ARR and recent activity. The best single call for 'how is my newsletter doing'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
range_daysNoWindow in days, for example 7, 30 or 90. Defaults to 30.
publicationNoWhich connected publication to act on, matched loosely against its hostname (for example 'example.substack.com' or just 'example'). Defaults to the first connected publication.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv3.0.1
    • removedInput schema / $schema
      Removed value: -"http://json-schema.org/draft-07/schema#"
    • removedInput schema / additionalProperties
      Removed value: -false
  2. First observedv2.1.1

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=true, so the safety profile is fully covered. The description adds window-scoping semantics ('start and end of the window') but nothing about permissions, rate limits, or freshness/staleness of the numbers.

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 compact sentences, front-loaded with the returned metrics and closed with the routing heuristic. Every clause earns its place; no filler.

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?

There is no output schema, so the description correctly carries the return burden by enumerating the headline figures. Both optional params are fully specified in the schema and all safety hints are in annotations, leaving nothing an agent needs before calling.

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% with only two optional parameters, both already documented in the schema (range_days window, publication hostname matching). The description adds no format or syntax detail beyond what the schema provides, so the baseline 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?

States a specific resource and enumerates the exact headline metrics returned (subscribers start/end, paid subscribers, ARR, recent activity). The phrase 'best single call for how is my newsletter doing' positions it against the many sibling analytics tools (get_analytics, get_revenue_summary, get_subscriber_count) 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?

Gives a clear usage scenario ('how is my newsletter doing') that routes the agent here for a general health check. It stops short of naming when NOT to use it or pointing at the more granular siblings for drill-down, so it is context without explicit alternatives or 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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