Sliq

Register Manual Linkedin Invites

register_manual_linkedin_invites

Only registers invites still pending; anything else comes back as not_pending — which can mean already accepted or never sent, so don't assume acceptance (check the prospect's linkedin_stage before messaging). Unmatched prospects that have a LinkedIn URL but no provider_id yet come back under resolving: Sliq looks up their profile in the background to identify them (in progress, not failed). Attach a resolved_profile trigger to act on each result — an already-connected one sends no acceptance event, so it won't engage on its own. Use this only after the prospects are tracked and the user has confirmed they sent the requests themselves.

Usually returns in seconds: the pending-invitations read stops as soon as every named prospect is matched, and recently hand-sent invites sit at the top of the list. When some prospects aren't found there, it pages the user's full backlog with paced gaps to protect their LinkedIn account — up to a couple of minutes on a large backlog — so warn the user about the possible wait only when registering invites sent long ago or likely already accepted, not for a just-sent batch.

Pass the specific people the user named in identifiers. Only set all_tracked=True when the user said they hand-invited the whole campaign — it registers every tracked prospect that has a real pending invitation, which can pull in an unrelated pending invite, so it's a deliberate opt-in rather than the default. Dict with registered / already_registered / not_pending / resolving / ambiguous / skipped lists, agent_has_accept_trigger (whether the agent has an accept trigger — a sequence campaign advances on accept via its flow regardless), and a summary message.

Input Schema

TableJSON Schema

Name	Required	Description
`node_id`	No	Optional. The sequence-DAG connection_request node the hand-sent CR satisfies (mirrors setup_linkedin_sequence's node_id) — stamped on the recorded row so its acceptance advances the flow onto the next node. Omit to auto-detect the agent's sole CR node; pass it explicitly on a multi-CR sequence. Rejected with ModelRetry if it isn't a connection_request node in the agent's sequence.
`agent_id`	Yes	The agent whose tracked prospects were manually invited.
`all_tracked`	No	Register every tracked prospect in the agent — use only when the user invited the whole campaign by hand.
`identifiers`	No	LinkedIn URLs, provider_ids, or names of the people the user invited by hand. Required unless `all_tracked` is set.

Schema Changelog

Changes observed during successful MCP inspections.

Changed5 schema fields changedSep 23, 2026

addedInput schema / properties / agent_id

Added value: +{
+  "description": "The agent whose tracked prospects were manually invited.",
+  "type": "integer"
+}

changedInput schema / properties / all_tracked / description

Previous value: -"Register every tracked prospect in the task — use only when the user\ninvited the whole campaign by hand."New value: +"Register every tracked prospect in the agent — use only when the user\ninvited the whole campaign by hand."

changedInput schema / properties / node_id / description

Previous value: -"Optional. The sequence-DAG connection_request node the hand-sent CR\nsatisfies (mirrors setup_linkedin_sequence's node_id) — stamped on the recorded\nrow so its acceptance advances the flow onto the next node. Omit to auto-detect\nthe task's sole CR node; pass it explicitly on a multi-CR sequence. Rejected with\nModelRetry if it isn't a connection_request node in the task's sequence."New value: +"Optional. The sequence-DAG connection_request node the hand-sent CR\nsatisfies (mirrors setup_linkedin_sequence's node_id) — stamped on the recorded\nrow so its acceptance advances the flow onto the next node. Omit to auto-detect\nthe agent's sole CR node; pass it explicitly on a multi-CR sequence. Rejected with\nModelRetry if it isn't a connection_request node in the agent's sequence."

removedInput schema / properties / task_id

Removed value: -{
-  "description": "The task whose tracked prospects were manually invited.",
-  "type": "integer"
-}

changedInput schema / required

Previous value: -[
-  "task_id"
-]New value: +[
+  "agent_id"
+]

First observedSep 15, 2026

TDQS

A4.7/5.0

Behavior5/5

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

The annotations only say readOnlyHint=false and destructiveHint=false; the description adds rich behavioral context: it validates against real pending invitations, can return not_pending for accepted-or-never-sent invites, performs background resolution for unmatched prospects, paces backlog reads to protect the account, and may take minutes on large backlogs. This substantially exceeds what annotations convey.

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 long but every block earns its place: core behavior, edge-case semantics, latency expectations, and parameter usage each get their own focused segment. It is front-loaded with the main purpose and uses a <returns> block for output instead of burying it in prose.

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?

For a mutating tool with four params, no output schema, and several edge cases, the description covers return categories, latency, non-pending semantics, background resolution, and acceptance-trigger behavior. No key decision an agent needs to invoke it safely is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3; the description adds meaning by clarifying that identifiers should be 'the specific people the user named' and that all_tracked=True is a deliberate opt-in that can pull in unrelated pending invites. It doesn't add param semantics for node_id beyond the schema, but the extra context for the other parameters justifies a 4.

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 opens with a specific verb and object: 'Register connection requests the user sent by hand so Sliq runs the accept→message sequence for them.' It further differentiates the operation by describing verification against LinkedIn's pending invitations, provider_id recovery, and cancellation of the redundant queued request, which separates it from queue-management siblings.

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?

It gives explicit when-to-use conditions ('Use this only after the prospects are tracked and the user has confirmed they sent the requests themselves') and a clear when-not/all_tracked caveat. It does not name an alternative sibling tool, so an agent must infer how this differs from related queue-management tools; still, the conditions are concrete enough to select it correctly.

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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