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Glama

Social Fetch

by io.github.social-freak-ltd

List Hacker News user submissions

hackernews_user_submissions_list
Read-only

List a Hacker News user's stories, polls, and jobs (newest first). Accepts a username. Returns a list (use page/pageSize when paginated).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoZero-based page index (maximum 50 pages, up to 1,000 hits).
limitNoHits per page (1–50). Default: 20. Prefer this over `pageSize`.
contextYesDescribe the user's underlying goal in one sentence — not the tool you are calling.
pageSizeNoDeprecated alias for `limit`. When both are set, `limit` wins.
usernameYesHacker News username (case-sensitive).
llm_modelYesThe exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
conversation_idNoEcho the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed5 schema fields changed
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / context
      Added value: +{
+  "description": "Describe the user's underlying goal in one sentence — not the tool you are calling.",
+  "type": "string"
+}
    • addedInput schema / properties / conversation_id
      Added value: +{
+  "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.",
+  "type": "string"
+}
    • addedInput schema / properties / llm_model
      Added value: +{
+  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "username"
-]New value: +[
+  "username",
+  "context",
+  "llm_model"
+]
  2. Changed1 schema field changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  3. Changed2 schema fields changed
    • addedInput schema / properties / limit
      Added value: +{
+  "description": "Hits per page (1–50). Default: 20. Prefer this over `pageSize`.",
+  "maximum": 50,
+  "minimum": 1,
+  "type": "integer"
+}
    • changedInput schema / properties / pageSize / description
      Previous value: -"Hits per page (1–50). Default: 20."New value: +"Deprecated alias for `limit`. When both are set, `limit` wins."
  4. Changed1 schema field changed
    • addedInput schema / additionalProperties
      Added value: +false
  5. Added

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint/openWorldHint, so the safety profile is covered. The description adds useful behavioral details beyond that: output is a list, results are newest-first, and pagination is supported via page/pageSize. This is meaningful practical guidance for invoking and interpeting the result.

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, front-loaded sentences with no filler. The core action and scope appear first, ordering is stated succinctly, and pagination guidance is one clause. Every sentence earns its place.

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 simple filtered-list tool with rich schema coverage and read-only annotations, the description covers what is listed, the order, and pagination. There is no output schema, but the item classes (stories, polls, jobs) are named, so an agent has enough shape. Minor omission: it could have noted that pageSize is deprecated in favor of limit.

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 baseline is 3 and the description need not restate every param. It adds a useful hint that username is the core input and that pagination may apply, but it does not deepen parameter semantics beyond schema, and its 'page/pageSize' wording leans toward the deprecated pageSize param rather than the preferred limit.

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 clearly states the verb, resource, and scope: it lists a specific Hacker News user's submissions, defined as stories, polls, and jobs, newest first. This explicit content-type breakdown distinguishes it from sibling tools like hackernews_user_comments_list and hackernews_user_favorites_list without needing to open their schemas.

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 description makes the usage context clear: use this tool when the agent needs a user's submitted stories, polls, or jobs. It does not explicitly name alternatives or state exclusions, but the defition of submissions as stories/polls/jobs implicitly separates it from user comments and favorites.

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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TDQS

B3.4/5.0
Disambiguation5/5

Each tool is clearly scoped to a specific platform and action (e.g., facebook_post_get vs instagram_post_get). Descriptions explicitly differentiate similar tools across platforms, and within-a-platform tools like tiktok_search_videos_list vs tiktok_search_hashtag_list have clear disambiguation notes.

Naming Consistency5/5

All 167 tools follow a strict `platform_resource_action` pattern (e.g., youtube_video_comments_list). No mixing of styles—snake_case throughout, with consistent verb ordering (get, list, search, etc.).

Tool Count2/5

The server has 167 tools, which is far beyond the typical well-scoped range of 3-15. While the broad multi-platform scope justifies many tools, this extreme number makes the tool surface overwhelming and difficult for an agent to navigate efficiently.

Completeness4/5

The tool set covers a wide range of platforms and operations including profile retrieval, post/video fetching, comments, search, transcripts, and ad library access. Minor gaps exist (e.g., no Facebook events or LinkedIn messaging), but the surface is comprehensive for a read-only data aggregation use case.

Resources