Skip to main content
Glama
Sign In
Substack MCP Server

Substack MCP Server

by cotletdev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Recommend publication

recommend_publication
DestructiveIdempotent

Add a publication to your Substack Recommendations with an optional reader-facing blurb, or remove an existing recommendation.

Instructions

Recommend another publication to your readers, or stop recommending it.

Adds a publication to your Recommendations, as Growth > Recommendations does: Substack suggests it to people who subscribe to you and notifies its author. blurb is the short reason readers see. With remove it takes the recommendation back.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "publication_id": "<publication_id>"
    },
    "name": "recommend_publication"
  }
}

Input Schema

NameRequiredDescriptionDefault
blurbNoWhy you recommend it, shown to your readers (optional).
removeNoStop recommending it instead. Default: false.
publication_idYesPublication ID, e.g. from search_substack posts.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

Goes meaningfully beyond the annotations: it discloses the outward-facing consequences that a caller could not infer from hints alone — the publication is suggested to people who subscribe to the caller and the recommended author is notified. It also clarifies that the same tool reverses the effect, which supports the idempotentHint. It does not describe the response payload or how un-recommending interacts with an existing blurb.

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?

Three short sentences, front-loaded with the action and its inverse, then the mechanics. Slight overlap between 'Recommend another publication to your readers' and 'Adds a publication to your Recommendations', but nothing is wasteful.

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 three-parameter toggle tool with no output schema and rich annotations, the description covers the effect, the side effects, and the reversal path. The remaining gap — what a successful call returns and whether an existing recommendation's blurb is replaced — is minor but real.

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 all three parameters are already documented in the schema, and the description largely restates them ('blurb is the short reason readers see', 'with remove it takes the recommendation back'). The blurb phrasing adds a small amount of audience context, but this is baseline-level value, not compensation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (recommend / stop recommending) applied to a specific resource (another publication), and the dual-mode nature is spelled out up front. It does not name its read-side sibling list_recommendations, but the action is distinct enough from every other sibling in the set that confusion is unlikely.

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

Usage Guidelines3/5

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

The UI anchor 'as Growth > Recommendations does' and the note that `remove` reverses the action give an implied picture of when to use each mode. However, there is no explicit when-to-use framing, no prerequisite (e.g. must be subscribed/authorized), and no pointer to list_recommendations for checking current state.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools