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dzigi00

LinkedIn Automation MCP Server

by dzigi00

Search Conversations

search_conversations
Read-only

Search LinkedIn messages by keyword to locate relevant conversations. Optionally set a result limit for focused retrieval.

Instructions

Search messages by keyword.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of search-result rows to enumerate as conversation references (1-50, default 20). Each enumeration selects the row in LinkedIn's UI and may mark it as read, so a low cap is preferable for noisy queries.
keywordsYesSearch keywords to filter conversations

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

Annotations declare readOnlyHint=true and openWorldHint=true, which already cover the safety profile. The description adds no additional behavioral context beyond the schema, and it does not contradict the annotations. However, the schema's parameter description for 'limit' mentions that enumeration may mark messages as read, a side effect not reflected in the description, which prevents a higher score.

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?

The description is a single, focused sentence with no filler or repetition. It conveys the core purpose efficiently and earns its place without unnecessary detail.

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

Completeness4/5

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

For a simple search tool with an output schema and full parameter descriptions, the minimal description is mostly adequate. However, it does not clarify the scope of the search (e.g., across all conversations or current inbox) and omits the side-effect warning found in the schema. These gaps are minor given the simplicity, but prevent a perfect score.

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%, with both 'keywords' and 'limit' fully documented. The description adds no extra meaning for parameters beyond what the schema already provides. This aligns with the baseline score for full schema coverage.

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 'Search messages by keyword' clearly states the tool's action (search) and resource (messages), which is sufficient and distinct from sibling tools like search_people or get_conversation. However, it does not explicitly differentiate from alternatives such as get_inbox or get_conversation, so it lacks the extra distinction that would earn 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 gives no guidance on when to use this tool versus alternatives. There is no mention of use cases, exclusions, or context where other tools would be more appropriate. The existence of get_inbox and get_conversation suggests potential overlap, but the description does not address it.

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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