Influship

get_creator

Read-only

Fetch the full record for a single creator by ID or exact platform username.

Use this when you already have either:

a canonical creator UUID returned by search_creators, semantic_search_creators, autocomplete_creators, or find_lookalike_creators; or
an exact platform+username pair such as platform "instagram" and username "niickjackson".

Pass include: ['profiles'] to also receive the creator's social profile summaries when using a creator UUID. For platform+username inputs, this tool resolves through the profile endpoint and returns the profile record plus the underlying creator record, so you already get the matched profile context.

Examples:

User: "Get creator 123e4567-e89b-12d3-a456-426614174000" -> call with id.
User: "Get @niickjackson on Instagram" -> call with platform "instagram" and username "niickjackson", or use get_profile if profile metrics are the main need.
User: "Tell me about @niickjackson and include his profiles" -> use platform "instagram" and username "niickjackson"; then use get_profile/get_posts for platform-specific metrics and content if needed.

Use lookup_profiles for batch exact profile lookups.

Input Schema

Name	Required	Description
`id`	No	Creator unique identifier
`context`	Yes	Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
`include`	No	Additional data to include in response
`platform`	No	Social platform for the username.
`username`	No	Public username or handle. A leading @ is accepted.
`llm_model`	Yes	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.
`conversation_id`	No	Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.

Output Schema

Name	Required	Description	Default
`ok`	No
`data`	No
`count`	No
`results`	No
`has_more`	No
`not_found`	No
`next_cursor`	No
`suggested_followups`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed5 schema fields changedNov 5, 2026

removedInput schema / additionalProperties
```
Removed value: -false
```

addedInput schema / properties / context

Added value: +{
+  "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\"",
+  "type": "string"
+}

addedInput schema / properties / conversation_id

Added value: +{
+  "description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.",
+  "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"
+}

addedInput schema / required

Added value: +[
+  "context",
+  "llm_model"
+]

Changed2 schema fields changedOct 23, 2026
- removedOutput schema / properties / next_cursor / anyOf
```
Removed value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "null"
-  }
-]
```
- addedOutput schema / properties / next_cursor / type
```
Added value: +[
+  "string",
+  "null"
+]
```
Changed3 schema fields changedAug 27, 2026
- changedInput schema / properties / username / description
```
Previous value: -"Platform username or handle without the @ sign."New value: +"Public username or handle. A leading @ is accepted."
```
- removedInput schema / properties / username / maxLength
```
Removed value: -50
```
- removedInput schema / properties / username / minLength
```
Removed value: -1
```
First observedMay 28, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds real behavior beyond that: include:['profiles'] only augments UUID lookups, while platform+username resolves through the profile endpoint and returns both the profile record and the underlying creator record. No error or not-found behavior is discussed, keeping it short of a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core statement, then a bulleted list of accepted inputs, then examples — a scannable structure. The three worked examples are somewhat repetitive and the second one mostly restates alternative routing already covered, but nothing is misleading or padded with filler.

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?

Output schema exists, so return values need not be explained, and the description still covers the dual input modes and the differing response shape for each. For a 7-parameter read tool with formal annotations, an agent has everything needed to select and call it 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 coverage is 100%, so all seven parameters are already documented. The description does add meaning beyond the schema for the include parameter (works only with a UUID) and for how platform+username are combined, but says nothing about the analytics-only parameters context, llm_model, and conversation_id. Baseline 3 plus partial added value fits.

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 first sentence names a specific verb and resource ("Fetch the full record for a single creator") and states the two accepted lookup keys (UUID or exact platform+username). This clearly separates it from search_creators, semantic_search_creators, get_profile, and lookup_profiles, which are all named.

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?

Explicit preconditions are given ("Use this when you already have either...") along with the upstream tools that produce the UUID. Alternatives are named with selection criteria: get_profile/get_posts for platform metrics, lookup_profiles for batch, and a worked example routing the "profile metrics" case away from this tool.

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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