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Glama

Social Fetch

by io.github.social-freak-ltd

Search SocialFetch docs

docs_search
Read-only

Search SocialFetch docs for API/SDK questions. For response fields use docs_read (defaults to outline). For cross-platform author/timestamp naming, search field crosswalk or read /product/ask-ai/field-crosswalk.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of docs results to return.
queryYesNatural-language description of what to find in SocialFetch docs (at least 2 characters).
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.
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.
currentDocsPathnameNoOptional current SocialFetch docs pathname, such as /docs/sdk or /product/guides/..., to boost nearby results.

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: -[
-  "query"
-]New value: +[
+  "query",
+  "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. Changed1 schema field changed
    • changedInput schema / properties / currentDocsPathname / description
      Previous value: -"Optional current SocialFetch docs pathname, such as /docs/sdk, to boost nearby results."New value: +"Optional current SocialFetch docs pathname, such as /docs/sdk or /product/guides/..., to boost nearby results."
  4. First observed

TDQS

A4.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds useful routing context (docs_read defaults to outline, field-crosswalk path) but does not describe docs_search's own result behavior, ranking, or output format. There is no contradiction with annotations, so the score stays at a solid 3.

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?

Two sentences with the main action front-loaded and no filler. The routing to docs_read and the specific field-crosswalk path are compact and each 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 read-only search tool, the description adequately covers when to use it, when not to use it, and a concrete crosswalk route. The schema fully documents all six parameters, including required context and llm_model. The only notable absence is a description of what a result looks like, which keeps this from a 5.

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 baseline is 3. The description adds one domain-specific query example ('field crosswalk') but does not add meaning to the query or context parameters beyond what the schema already provides.

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 states a specific verb ('Search'), a concrete resource ('SocialFetch docs'), and a clear scope ('API/SDK questions'). It also distinguishes itself from the sibling docs_read by explicitly saying response-field lookups should use docs_read instead.

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?

It clearly scopes when to use this tool (API/SDK questions) and gives explicit alternatives: 'For response fields use docs_read' and a specific recipe for cross-platform field naming ('search `field crosswalk` or read /product/ask-ai/field-crosswalk'). This leaves little to inference.

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