Crawlora MCP

datasets_substack_search

Read-only

Search stored Substack writer profiles and publications by q, kind, identifier, or publication language. Returns a page of validated public snapshots with uid and hydrated_at; default 20 records, max 100. Coverage is incomplete and snapshots are not periodically revalidated. Costs one credit per returned record, minimum two. Use datasets_substack_item with an exact uid or datasets_substack_facets to discover filter values.

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": "datasets_substack_search"
  }
}

Input Schema

Name	Required	Description
`q`	No	Optional full-text query over public profile/publication name, handle, bio, subdomain, custom domain and hero text; max 256 characters.
`kind`	No	Optional record kind. Allowed values: user, publication.
`page`	No	Result page number, 1-based, default 1; page times page_size must not exceed 10000.
`sort`	No	Optional sort order. Allowed values: relevance, name_asc, hydrated_at_desc, hydrated_at_asc. Defaults to relevance with q, otherwise hydrated_at_desc.
`language`	No	Optional exact publication language filter; use facet=language to discover stored values.
`page_size`	No	Page size, default 20, max 100; page times page_size must not exceed 10000.
`identifier`	No	Optional exact Substack profile handle or publication host from a dataset row, max 253 characters.

Output Schema

Name	Required	Description	Default
`data`	Yes	The tool result payload (shape varies per tool; see each tool's docs resource).

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 7, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Goes well beyond the readOnlyHint/openWorldHint annotations: discloses result shape (uid, hydrated_at), pagination defaults and caps, data-quality caveats (incomplete coverage, snapshots not revalidated), and a non-obvious cost model (one credit per record, minimum two). These are exactly the facts an agent needs before spending credits.

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 dense sentences, front-loaded with purpose, then return shape, caveats, cost, and sibling routing. Nothing is padded and no sentence is redundant.

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?

Covers the gaps structured data does not: partial coverage, snapshot staleness, and credit pricing, while routing to the item and facets siblings. With an output schema already defining return values, nothing an agent needs to call this correctly is missing.

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 coverage is 100%, so character limits, enum values, sort defaults and page*page_size bounds are already documented in the schema. The description only restates parameter names and adds no syntax or format detail beyond the schema, which earns the baseline 3.

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 ("Search stored Substack writer profiles and publications") and enumerates the accepted filter axes (q, kind, identifier, language). It is immediately distinguishable from siblings like datasets_substack_item, which it names for exact-uid lookups.

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

Usage Guidelines5/5

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

Explicitly routes the agent: use this tool for filtered discovery, datasets_substack_item when an exact uid is known, and datasets_substack_facets to discover valid filter values. No ambiguity about which sibling to pick.

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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