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Glama

Social Fetch

by io.github.social-freak-ltd

Generate web page HTML

web_html_generate
Read-only

Fetch cleaned HTML for a web page URL.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesWeb page URL to fetch.
contextYesDescribe the user's underlying goal in one sentence — not the tool you are calling.
waitForNoWait for a CSS selector before extraction. Must be prefixed with "css:" (e.g. css:main). JavaScript wait conditions are not supported.
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.
scanFullPageNoWhen true, scroll the page to load dynamically appended content (infinite scroll). Default false.
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: -[
-  "url"
-]New value: +[
+  "url",
+  "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 / scanFullPage
      Added value: +{
+  "anyOf": [
+    {
+      "type": "boolean"
+    },
+    {
+      "enum": [
+        "0",
+        "1",
+        "true",
+        "false"
+      ],
+      "type": "string"
+    }
+  ],
+  "description": "When true, scroll the page to load dynamically appended content (infinite scroll). Default false."
+}
    • addedInput schema / properties / waitFor
      Added value: +{
+  "description": "Wait for a CSS selector before extraction. Must be prefixed with \"css:\" (e.g. css:main). JavaScript wait conditions are not supported.",
+  "maxLength": 200,
+  "type": "string"
+}
  4. Changed1 schema field changed
    • addedInput schema / additionalProperties
      Added value: +false
  5. First observed

TDQS

B3.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, covering the safety and external-access profile. The description adds 'cleaned HTML' as a behavior, which gives some transparency, but it does not explain what cleaning entails, whether JavaScript is executed, or any rate limits/caching behavior. No contradiction with annotations.

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?

The description is a single clear sentence with zero waste. The core action, target, and output format are front-loaded, making it easy for an agent to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with six parameters and no output schema, the description conveys the essential function but omits important operational context: no guidance on dynamic page handling, no explanation of 'cleaned', and no mention of when HTML output is preferable to markdown or text. The well-documented schema covers paramters but not the behavioral context an agent would benefit from.

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 fully documents all six parameters. The tool description adds no parameter-specific meaning beyond 'Fetch cleaned HTML', which is the baseline case where schema carries the burden.

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?

The description states a specific verb and resource: 'Fetch cleaned HTML for a web page URL.' It clearly identifies the output format (HTML) and distinguishes it from the markdown-focused sibling tool. The title says 'Generate' rather than 'Fetch', which is slightly misleading, but the description itself is unambiguous.

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

Usage Guidelines2/5

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

No guidance is given about when to use this tool versus alternatives such as web_markdown_generate, web_extract_run, or web_screenshot_capture. The description provides no conditions, exclusions, or context that would help an agent choose among the many web_* sibling tools.

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