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Glama

Social Fetch

by io.github.social-freak-ltd

Create a monitor

monitors_create

Watch a social account or search and get a signed webhook when new content appears. Runs a synchronous baseline check on create — the response includes what's there right now, and you'll only get webhooks for items after that.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
typeYesinterval or cron.
paramsYesParams for the source, e.g. { handle: "elonmusk" }.
contextYesDescribe the user's underlying goal in one sentence — not the tool you are calling.
minutesNoRequired when type is interval.
timezoneNoIANA timezone, required when type is cron.
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.
expressionNoCron expression, required when type is cron.
operationIdYesA watchable operationId from monitors_sources_list.
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.
spendCapCreditsNo
webhookEndpointIdNo

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: -[
-  "operationId",
-  "params",
-  "type"
-]New value: +[
+  "operationId",
+  "params",
+  "type",
+  "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. Added

TDQS

A3.9/5.0
Behavior4/5

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

Despite sparse annotations, the description adds material behavioral context: a synchronous baseline check on create, a response reflecting current state, and webhooks only for items appearing after that baseline. The mention of a 'signed' webhook is also a useful security-relevant detail. This goes well beyond the readOnlyHint/openWorldHint flags without contradicting them.

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 zero waste: the first front-loads the core purpose and the second discloses the crucial baseline-check behavior. Every clause earns its place, and the structure places the most decision-relevant fact (baseline semantics) right after the definition.

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 12-param, 5-required mutation tool with no output schema, the description conveys the create-time workflow but omits the scheduling model, the interaction between type and its conditional params (minutes vs expression/timezone), and what webhookEndpointId binding entails. The schema covers param-level details, yet an agent still lacks a clear picture of what a configured monitor does over time beyond 'webhook on new content.'

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 75%, with good inline descriptions for operationId, type, minutes, timezone, expression, context, and llm_model, so the schema carries most of the param-documentation burden. The description adds workflow-level meaning by linking the watch target (operationId) and delivery mechanism (webhookEndpointId) to the returned behavior, but it does not explain the interval-vs-cron conditional requirements or spendCapCredits semantics. Baseline 3 is appropriate since the schema already does the heavy lifting.

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 and resource ('Watch a social account or search and get a signed webhook when new content appears') that squarely defines what creating a monitor produces. The webhook-on-new-content behavior distinguishes it from one-shot data getters and from sibling monitors_* tools like monitors_list and monitors_get, which operate on existing monitors.

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

Usage Guidelines3/5

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

The description clearly implies the use case — ongoing monitoring of a source for new content — but never says when not to use it or names alternatives like monitors_trigger or the platform-specific getters for one-time fetches. The prerequisite link to monitors_sources_list exists only in the schema's operationId description, not in the tool description, so routing guidance is left 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