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Resolve an anonymous LinkedIn link

resolve_profile

Resolve obfuscated LinkedIn profile links into real profile URLs and identities. Use batch mode for up to 50 references to reveal who's behind anonymous profiles.

Instructions

Reveal who is behind an anonymous LinkedIn profile link. BILLABLE — about $0.0005 per RESOLVED profile (an unresolvable reference is free) — duplicates in one batch are billed per row, so deduplicate first (Tier 0 list price; your account may pay a different rate — call get_balance for your real prices, and read cost.amount_charged in every response for what was actually spent). Takes the obfuscated links that Sales Navigator leaves in exports, CRMs and ad platforms — linkedin.com/in/ACwAA… — plus Sales Navigator lead URLs, bare profile ids and urns, and returns the real profile URL and identity. Pass profiles (up to 50) to do a batch in one call. The id it returns is the same identifier enrich_lead, generate_email and find_phone accept, so this is the cheap first step before spending on a full record. Returns identity only — no location, company or work history; use enrich_lead for those. The numeric member id is NOT accepted as input (LinkedIn answers 403 to it); it comes back as linkedin_id.

Input Schema

TableJSON Schema

Name	Required	Description
`id`	No	Alias for `url` — same accepted values. Use whichever reads better.
`url`	No	A single LinkedIn person reference: profile URL of any flavour (including /in/ACwAA… and /sales/lead/…), a public identifier, an obfuscated id (ACwAA… or ACoAA…) or an urn. Matched case-insensitively.
`compact`	No	Default true: return a small per-profile summary (including the Generect `id`, which every later step accepts). Set false for the full raw record (~80 fields) — only worth it when you specifically need skills, education or other deep fields.
`profiles`	No	Batch mode: 1–50 references, mixed freely. One row per input, in input order, each either a resolved profile or {input, error}. Duplicates are billed per row — deduplicate first.
`timeout_ms`	No	Request timeout in milliseconds.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`cost`	No
`profiles`	No
`resolved`	No
`test_mode`	No
`test_mode_notice`	No
`vocabulary_warnings`	No
`deprecated_params_ignored`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed6 schema fields changedv0.12.2Oct 16, 2026

changedInput schema / $schema

Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"

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

changedOutput schema / $schema

Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"

addedOutput schema / properties / deprecated_params_ignored / propertyNames
```
Added value: +{
+  "type": "string"
+}
```
addedOutput schema / properties / profiles / items
```
Added value: +{}
```
addedOutput schema / properties / vocabulary_warnings / items
```
Added value: +{}
```

Addedv0.9.0Sep 24, 2026

TDQS

A4.2/5.0

Behavior3/5

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

Annotations declare readOnlyHint=false, openWorldHint=true, destructiveHint=false, so the safety profile is already partly carried. The description still goes well beyond them: per-row billing (~$0.0005, unresolved free), duplicate rows billed per row, cost.amount_charged in every response, and the hard 403 on numeric member ids. That is genuinely additive behavioral context.

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?

The core purpose is front-loaded in the first sentence and every sentence carries load. It is dense with parentheticals and repeats the billing/credits point, which costs a little readability, but nothing is padding.

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?

Even though an output schema exists, the description usefully explains the return shape (resolved profile vs {input,error}, the interop `id`, `linkedin_id`, compact vs ~80-field raw). Combined with the billing and input-constraint coverage, an agent has everything needed to 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 description coverage is 100%, so the baseline is 3 and the schema already documents id/url/compact/profiles/timeout_ms. The description does add meaning (batch up to 50 in one call, one row per input, duplicates billed per row, and the critical negative constraint that the numeric member id is rejected), which is more than restating the schema, but it largely mirrors what the properties already say.

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?

Specific verb+resource: 'Reveal who is behind an anonymous LinkedIn profile link.' It explicitly scopes what is returned (identity only, no location/company/work history) and names the sibling that covers the rest (enrich_lead), so an agent can separate it from enrich_lead, get_lead_by_url and find_phone without opening a schema.

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 routing: 'this is the cheap first step before spending on a full record' and 'use enrich_lead for those'. It also states operational prerequisites — deduplicate before batching, call get_balance for real prices — and names the downstream consumers (enrich_lead, generate_email, find_phone).

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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