linkedin-mcp

discard_proposal

Remove a rejected or unwanted proposal from local storage. This action only deletes the local draft and does not modify your LinkedIn profile.

Instructions

Delete a saved proposal that the human rejected or that is no longer wanted. Local file removal only — it never touches LinkedIn.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "proposal_id": "<proposal_id>"
    },
    "name": "discard_proposal"
  }
}

Input Schema

Name	Required	Description	Default
`proposal_id`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 25, 2026

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states that the tool is destructive ('Delete'), scopes behavior to local file removal only, and explicitly excludes external action (LinkedIn). This covers the key behavioral aspects for a simple deletion tool, though it doesn't mention reversibility or side effects.

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 sentence that front-loads the core action ('Delete a saved proposal'), then adds relevant conditions and scope. There is no wasted wording; every part adds value, making it highly concise and well-structured.

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 one-parameter delete tool with an output schema present, the description is sufficiently complete. It covers purpose, conditions, and scope. It doesn't detail error handling or edge cases, but those are not essential for basic usage. The scope and behavior are well communicated.

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 0% for the single parameter 'proposal_id', but the description implies the parameter is the identifier of the proposal to delete. It doesn't add explicit details about the parameter, but the intuitive meaning is clear from the context. Since there's only one parameter, the description's implication partially compensates for the lack of schema documentation.

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?

The description clearly states the operation ('Delete a saved proposal') and specifies the resource and conditions ('that the human rejected or that is no longer wanted'). It explicitly distinguishes from siblings by noting 'Local file removal only — it never touches LinkedIn,' preventing confusion with apply_proposal or other LinkedIn-related tools.

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

Usage Guidelines4/5

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

The description gives clear context for when to use the tool (when a proposal is rejected or unwanted) and clarifies what it does not do (never touches LinkedIn). It implicitly differentiates from apply_proposal (which would send to LinkedIn) and propose_edit (which edits). It could be stronger by naming alternatives, but the context is sufficient.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server