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datasets_substack_facets

Retrieve up to 50 count-sorted Substack dataset facet buckets by kind or language, with search-style filters, to discover indexed discovery corpus values.

Instructions

Discover Substack dataset values. Returns up to 50 count-sorted buckets for a stored-field facet over the Substack dataset, honoring the same filters as datasets_substack_search. Facets cover the indexed discovery corpus, which is not a complete directory.

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": {
      "facet": "<facet>"
    },
    "name": "datasets_substack_facets"
  }
}

Input Schema

Name	Required	Description
`q`	No	Full-text query over profile/publication fields, max 256 characters
`kind`	No	Record kind
`page`	No	Accepted for contract parity; facets aggregate all matching records
`sort`	No	Accepted for parity with search: relevance, name_asc, hydrated_at_desc, hydrated_at_asc
`facet`	Yes	Facet field
`language`	No	Exact publication language
`page_size`	No	Accepted for contract parity; facets aggregate all matching records
`identifier`	No	Exact public profile handle or publication host, max 253 characters

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.17.20Nov 12, 2026

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and delivers real behavior: a 50-bucket cap, count-based ordering, and the caveat that the corpus is an indexed discovery set rather than a complete directory. It omits auth/rate-limit context and the exact bucket shape, but the essential aggregation semantics are disclosed.

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?

Three short sentences, front-loaded with what is returned, then contract parity, then the corpus caveat. No redundant restatement of the name or title.

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 an eight-parameter, no-output-schema, unannotated tool, the description covers the key unknowns: result cap, ordering, filter reuse, and data-completeness limits. It does not describe the practical consequences of the ignored page/page_size parameters beyond what the schema says, leaving a small gap.

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 the schema already documents every parameter, including the page/page_size parity behavior. The description adds only that filters mirror datasets_substack_search, which is useful framing but no new per-parameter detail, so 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 verb and resource ('Discover Substack dataset values', 'count-sorted buckets for a stored-field facet') and scopes it to the Substack dataset while naming the sibling whose filtering contract it shares. An agent can distinguish it from datasets_substack_search without opening either 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?

The reference to datasets_substack_search establishes that record retrieval belongs to the sibling and this tool is for aggregation, which is clear contextual routing. It stops short of an explicit 'use X when Y' exclusion, so it is strong but not fully prescriptive.

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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