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by net.crawlora
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datasets_substack_facets

Read-only

Return up to 50 count-sorted buckets for one Substack snapshot field under the filters from datasets_substack_search. facet accepts kind or language; page, page_size, and sort do not change counts. Coverage is the incomplete stored public corpus. Use datasets_substack_search to retrieve matching profiles and publications.

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

NameRequiredDescriptionDefault
qNoOptional full-text query over public profile/publication name, handle, bio, subdomain, custom domain and hero text; max 256 characters.
kindNoOptional record kind. Allowed values: user, publication.
pageNoResult page number, 1-based, default 1; page times page_size must not exceed 10000.
sortNoOptional sort order. Allowed values: relevance, name_asc, hydrated_at_desc, hydrated_at_asc. Defaults to relevance with q, otherwise hydrated_at_desc.
facetYesrequired,Required facet. Allowed values: kind, language.
languageNoOptional exact publication language filter; use facet=language to discover stored values.
page_sizeNoPage size, default 20, max 100; page times page_size must not exceed 10000.
identifierNoOptional exact Substack profile handle or publication host from a dataset row, max 253 characters.

Output Schema

NameRequiredDescriptionDefault
dataYesThe tool result payload (shape varies per tool; see each tool's docs resource).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.3/5.0
Behavior5/5

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

Goes well beyond the readOnlyHint/openWorldHint annotations by disclosing the result cap (up to 50 buckets), the count-invariance of pagination and sort parameters, and, most valuably, that coverage reflects the incomplete stored public corpus rather than the full Substack universe. These are exactly the behavioral caveats an agent needs to interpret counts correctly.

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?

Four short sentences, front-loaded with the core behavior, then parameter caveats, then the data-coverage caveat, then the sibling pointer. No filler or repetition of schema text.

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?

Given an output schema exists, return value structure need not be explained; the description covers scope, filter inheritance, count semantics and corpus limitations. What remains unstated is whether the filter parameters from search combine here or how bucket keys/values relate to the dataset rows, a small gap for an 8-parameter tool.

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

Parameters4/5

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

With 100% schema coverage the baseline is 3, but the description adds real meaning: it identifies facet as the one required dimension restricted to kind or language, and explains that page, page_size and sort do not affect the returned counts. The remaining filter parameters (q, kind, language, identifier) are left to the schema.

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 and resource: return count-sorted facet buckets for one Substack snapshot field, with the facet dimension named. It is clearly differentiated from the row-returning sibling datasets_substack_search. Minor deduction only because 'snapshot field' is jargon that assumes familiarity with the dataset model.

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?

Explicitly routes the agent: use this tool for facet counts, and use datasets_substack_search to retrieve matching profiles and publications. It also tells the agent that page, page_size and sort are irrelevant to counts, which heads off a common misuse. It does not spell out when to prefer facets (e.g. discovering stored kind/language values before searching), but that is implied.

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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