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Jing-yilin

LinkedIn MCP Server

by Jing-yilin

search_geo_id

Find LinkedIn Geo IDs for location-based filtering by searching location names, enabling precise geographic targeting in LinkedIn data queries.

Instructions

Search LinkedIn Geo ID by location (for location-based filtering). Returns cleaned data in TOON format.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
searchYesLocation text to search
save_dirNoDirectory to save cleaned JSON data
max_itemsNoMaximum results (default: 10)
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'Returns cleaned data in TOON format,' which adds some context about the output format. However, it lacks details on rate limits, authentication needs, error handling, or whether this is a read-only operation (implied by 'Search' but not explicit). For a search tool with zero annotation coverage, this is insufficient.

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 description is concise and front-loaded: it states the core purpose in the first phrase and adds output format in the second. Both sentences earn their place by providing essential information without redundancy. It could be slightly more structured (e.g., separating purpose from output details), but it's efficient overall.

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?

Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is partially complete. It covers the purpose and output format, but lacks behavioral details (e.g., safety, limits) and usage guidelines. With no output schema, the mention of 'TOON format' helps, but more context on what 'cleaned data' entails would improve completeness. It's adequate but has clear gaps.

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?

The schema description coverage is 100%, so the schema already documents all three parameters ('search', 'save_dir', 'max_items') with descriptions. The description adds no additional parameter semantics beyond what's in the schema. According to the rules, with high schema coverage (>80%), the baseline is 3 even with no param info in the description.

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's purpose: 'Search LinkedIn Geo ID by location (for location-based filtering).' It specifies the verb ('Search'), resource ('LinkedIn Geo ID'), and context ('for location-based filtering'). However, it doesn't explicitly distinguish this from sibling tools like 'search_companies' or 'search_profiles' beyond the 'Geo ID' focus, which is why it's a 4 rather than a 5.

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?

The description provides minimal usage guidance. It mentions 'for location-based filtering' which hints at context, but doesn't specify when to use this tool versus alternatives (e.g., other search tools in the sibling list) or any prerequisites. There's no explicit 'when' or 'when not' guidance, leaving the agent to infer usage based on the purpose alone.

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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