lookup_linkedin_profileTurn a LinkedIn profile URL into clean structured data: name, current role and company, and location. Spends 2 credits, refunded if the profile cannot be retrieved.
| Name | Required | Description | Default |
|---|---|---|---|
| linkedin_url | Yes | A LinkedIn profile URL, e.g. https://linkedin.com/in/… |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations, the description discloses that the tool spends 2 credits and refunds them if the profile cannot be retrieved, which is important cost-related behavior. It also communicates the return shape. Annotations already indicate readOnlyHint=false and idempotentHint=false, so the description adds meaningful context 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences deliver all key information: input, output, and credit cost/refund behavior. There is no filler or redundancy, and the most useful content is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with no output schema, the description is complete: it defines the input, the output fields, and the credit cost/refund behavior. No critical information for calling the tool correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents the single linkedin_url parameter. The description adds no new parameter-level syntax or constraints beyond what the schema provides, which aligns with the baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Turn'), the resource ('a LinkedIn profile URL'), and the specific output fields (name, current role/company, location). It does not explicitly distinguish itself from sibling tools like find_linkedin_profile or reveal_profile, which keeps it from a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the right usage context: use this when you already have a LinkedIn profile URL and want structured profile data. It does not state when not to use it or name alternatives, so it stops short of full routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct purposes, but pairs like company_search/lookup_company and find_linkedin_profile/lookup_linkedin_profile could cause selection errors. The free-search versus paid-unlock flow (people_search/find_people/reveal_profile) is well-differentiated by detailed descriptions.
Naming mixes verb_noun patterns (find_people, fetch_email) with noun_verb patterns (company_search, people_search), and uses overlapping verbs like find, lookup, search, and fetch. The pattern is readable but not consistently predictable.
With 12 tools, the set is well-scoped for a people search and email enrichment service. Each tool covers a distinct operation, from free search to paid profile unlock, email verification, and credit checking, without unnecessary bloat.
The toolset covers the core lifecycle: free search, paid profile unlock, email fetch, verification, and reverse lookup. Minor gaps exist, such as not being able to fetch an email later for a profile unlocked via reveal_profile unless include_email is set initially, but agents can work around these.