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Substack MCP Server

Substack MCP Server

by cotletdev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

List recommendations

list_recommendations
Read-onlyIdempotent

Lists publications you recommend or that recommend you, with subscriber counts and mutual status; optionally checks whether you recommend a specific publication.

Instructions

Publications you recommend, or that recommend you, with the subscribers each sent.

Returns Audience > Recommendations: with direction given, the publications you recommend; with received, the ones recommending you, and whether you recommend them back (mutual). subscribers_sent counts subscribers the recommendation brought. The table is rebuilt by Substack in the background, so a recommendation made minutes ago may be missing while counts already include it; publication_id checks one recommendation of yours directly.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_recommendations"
  }
}

Input Schema

NameRequiredDescriptionDefault
directionNogiven (publications you recommend) or received (that recommend you). Default: given.
publication_idNoCheck one publication instead: does yours recommend it (given only).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior5/5

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

Beyond the read-only and idempotent annotations, the description discloses important eventual-consistency behavior: the table is rebuilt in the background, recent recommendations may be missing while counts already include them. That caveat is operationally valuable and not captured by annotations.

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?

The definition is front-loaded and contains no filler, but the second sentence is long and combines output fields, background rebuild caveats, and parameter behavior. It is efficient overall, though the density reduces scannability slightly.

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?

The description explains what is returned, including direction-specific results, mutual status, and subscriber counts, and it covers the main behavioral caveat. It does not mention pagination, limits, or whether the list is ever truncated, which is a notable gap for a list-style tool with no output schema.

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 both parameters are already documented, including the enum values and default for `direction` and the `given only` restriction on `publication_id`. The description reinforces these meanings and adds related output context such as mutual recommendations, but it does not add substantial new parameter semantics 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 names the specific resource and distinguishes both directions: publications the user recommends versus publications that recommend the user. It also clarifies that subscribers each sent are included, which makes the tool’s scope concrete and distinguishable from adjacent recommendation actions.

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?

It explains how to interpret and use the `direction` values and how `publication_id` checks one recommendation directly. It does not explicitly contrast with siblings such as `recommend_publication` or `list_subscriptions`, but the context is otherwise clear.

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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