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Read SocialFetch docs page

docs_read
Read-only

Read a corpus page by docsPathname from docs_search. Defaults to mode=outline (compact field digest). Pass mode=full for Examples/SDK snippets. Accepts /docs/... and /product/... pathnames (including /product/ask-ai/field-crosswalk).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNooutline (default) = compact params/credits/field digest. full = complete page including Examples.outline
contextYesDescribe the user's underlying goal in one sentence — not the tool you are calling.
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.
docsPathnameYesCorpus pathname from docs_search, e.g. /docs/api/v1/tiktok/videos/get or /product/ask-ai/field-crosswalk. Trailing .mdx accepted.
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. Changed4 schema fields changed
    • 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: -[
      -  "docsPathname"
      -]New value: +[
      +  "docsPathname",
      +  "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
    • changedInput schema / properties / docsPathname / description
      Previous value: -"SocialFetch docs pathname, such as /docs/quickstart, /docs/sdk, or /docs/api/v1/tiktok/profiles/handle. A trailing .mdx is accepted."New value: +"Corpus pathname from docs_search, e.g. /docs/api/v1/tiktok/videos/get or /product/ask-ai/field-crosswalk. Trailing .mdx accepted."
    • addedInput schema / properties / mode
      Added value: +{
      +  "default": "outline",
      +  "description": "outline (default) = compact params/credits/field digest. full = complete page including Examples.",
      +  "enum": [
      +    "outline",
      +    "full"
      +  ],
      +  "type": "string"
      +}
  4. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false. The description adds useful behavioral context beyond these annotations: it explains what each mode returns (compact field digest vs. full page including Examples/SDK snippets) and specifies the accepted pathname patterns. It does not describe error handling or auth requirements, but the readOnly annotation already covers the safety profile and the description provides meaningful mode semantics.

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 sentences, each carrying essential information: the action and source, the mode behavior, and the accepted pathname formats. No filler or redundant restatement of the tool name.

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?

Despite having no output schema, the description gives an adequate sense of the return format for both modes (compact field digest vs. full page with Examples). It clearly ties the required docsPathname to docs_search, documents mode variants, and the schema covers the remaining required params (context, llm_model) with precise instructions. An agent has enough to invoke the tool correctly.

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 all five parameters thoroughly. The description adds minor value by explicitly tying docsPathname to docs_search and noting acceptable pathname prefixes, but this largely echoes the schema's own examples and the mode param description. Baseline 3 is appropriate.

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 tool's function: read a corpus page using a docsPathname obtained from docs_search. It distinguishes itself from the sibling docs_search tool by specifying the read action, the accepted pathname families, and the mode options, leaving no ambiguity about what it does.

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 explains when to use the tool (after obtaining a pathname from docs_search) and provides specific guidance on choosing mode=outline versus mode=full for different content needs. It references docs_search as the source of the pathname, which implicitly guides the agent away from using docs_read without a prior search, though it doesn't explicitly state 'use docs_search when you don't have a pathname'.

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.