Generect Live API MCP Server

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by generect

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TypeScript

Hybrid

Resolve an anonymous LinkedIn link

resolve_profile

Read-only

Convert obfuscated LinkedIn profile links from Sales Navigator exports into the real profile URL and identity, enabling cost-effective lead identification before enrichment.

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.

Addedv0.9.0Sep 24, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Annotations already provide readOnlyHint and openWorldHint, but the description adds substantial behavioral context: billable per resolved profile, free unresolvable references, duplicate billing per row, 403 on numeric member ids, batch row ordering, and output id compatibility. No contradiction with annotations.

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?

Dense but non-redundant. Front-loads the core purpose, then proceeds through billing, input types, batch mode, and output limitations in a logical order. Longer than typical but every sentence carries actionable information.

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?

Covers billing behavior, accepted and rejected inputs, batch semantics, and output limitations. With an output schema present, it correctly avoids detailing return fields. The timeout parameter is delegated to the schema, which is acceptable.

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?

Input schema covers 100% of parameters, so baseline is 3. The description adds extra semantics for `profiles` (batch size, billing per row, deduplication) and clarifies accepted input formats plus the rejected numeric member id. This enriches understanding beyond schema descriptions.

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?

States the specific action (reveal who is behind anonymous LinkedIn profile links) and the resource (anonymous LinkedIn links). It clearly distinguishes from siblings like enrich_lead by explicitly noting it returns identity only, and specifies accepted input types and output.

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?

Explicitly positions the tool as the cheap first step before enrich_lead, generate_email, and find_phone, and directs users to enrich_lead for location/company/work history. It also warns against using numeric member ids as input and instructs to call get_balance for real pricing.

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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