The Mine Works

find_linkedin_candidates

Recruiter sourcing: find LinkedIn profiles matching a role, skills, location, experience band, and optional target companies. Returns name, headline, current title/company, profile URL, matched skills, and a match-confidence score. No login required.

Input Schema

TableJSON Schema

Name	Required	Description
`skills`	No	Required skills (e.g. ["Python", "PyTorch"])
`location`	No	Location filter (e.g. "Bangalore", "Remote")
`maxYears`	No	Maximum years of experience
`minYears`	No	Minimum years of experience
`roleTitle`	Yes	Role to source for (e.g. "Senior Machine Learning Engineer")
`maxResults`	No	Max candidates (default 25)
`targetCompanies`	No	Only return candidates currently at these companies

TDQS

B3.3/5.0

Behavior3/5

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

The description notes 'No login required,' which is a useful behavioral trait. However, it does not disclose other important details such as rate limits, data freshness, pagination, or handling of empty results. With no annotations, the description carries the full burden but covers only minimal 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.

Conciseness5/5

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

Two concise sentences: first states purpose and criteria, second lists output and login requirement. No wasted words, efficiently communicates core functionality.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers basic functionality and output fields, but lacks guidance on usage boundaries, error cases, or limitations. Given 7 parameters, no output schema, and no annotations, the description is adequate but leaves gaps in completeness (e.g., ordering, pagination, match confidence details).

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 baseline is 3. The description maps to parameters (role, skills, location, experience band, target companies), adding the concept of 'experience band' for min/max years. However, it does not elaborate on parameter formats, constraints, or defaults beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool finds LinkedIn profiles matching specified criteria (role, skills, location, experience, target companies). It identifies the verb 'find' and resource 'LinkedIn profiles'. However, it does not differentiate from sibling tools like search_linkedin_employees or get_linkedin_profiles.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives. The phrase 'Recruiter sourcing' implies a use case, but no when-not-to-use or alternative suggestions are provided. Sibling tools with similar scope exist without distinction.

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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TDQS

A3.5/5.0

Disambiguation4/5

Most tools target unique data sources or specific actions (e.g., search_zillow vs. get_zillow_property_details are clearly sequential). A few LinkedIn-related tools (find_linkedin_candidates vs. search_linkedin_employees) have overlapping purposes but their descriptions clarify distinct use cases. Overall, confusion is minimal and descriptions resolve ambiguity.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern in snake_case, using verbs like search, get, find, scrape, analyze, lookup, resolve, and verify. The pattern is predictable across the entire set, making it easy for an agent to infer function from name.

Tool Count2/5

With 32 tools, the server exceeds the 'too many' threshold of 25+. While the broad scope of web data mining justifies some diversity, the count is unwieldy and could overwhelm an agent's selection process. A smaller, more focused set per domain would improve coherence.

Completeness4/5

The toolset covers a wide range of data retrieval needs: company research, real estate, job listings, academic research, and government records. For a read-only data aggregation service, there are no major lifecycle gaps, though some subdomains like social media scraping only cover Reddit and LinkedIn, missing other platforms. Overall, it is reasonably complete for its stated purpose.

Resources

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