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IzikStar

linkedin-agent-mcp

by IzikStar

Answer an application question

linkedin_provide_application_answer

Records your answer to a LinkedIn application question that cannot be auto-filled from your profile, keeping it local and not sending to LinkedIn.

Instructions

[PREPARE - changes local state only; performs NO external action] Records the USER's own answer to one application question that could not be established from their profile/CV/config. Local state only, nothing is sent to LinkedIn. Never invent the answer yourself: only call this with an answer the user actually gave. Once every question is answered the application becomes READY_TO_SUBMIT; check with linkedin_preview_application.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
answerYes
answerIdYes
applicationIdYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobIdYes
fieldsYes
statusYes
answersYes
cvLabelYes
applicationIdYes
unansweredCountYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior5/5

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

Annotations flag readOnlyHint=false and openWorldHint=false; the description confirms and enriches this by declaring the operation is local-state-only and that nothing is sent to LinkedIn, plus it discloses the downstream state machine effect (application becomes READY_TO_SUBMIT once all questions are answered). That is behavioral context beyond what the annotations convey.

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?

Front-loaded with the scope tag and operation, then constraints, then the state-transition and cross-reference. Every clause carries information; no filler.

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?

Output schema exists so return values need no explanation, and the safety/scope profile is fully covered. The only gap is that the agent isn't told how to obtain valid applicationId/answerId values, which is a real if minor omission for a 3-required-param tool.

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 0% and no parameter descriptions exist, so the description must compensate; it clarifies the intent of 'answer' (must be a real user-provided answer, not generated) but gives no guidance on where applicationId or answerId come from, nor the 2000/64-char limits. Partial compensation only.

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?

States a specific action ('records the USER's own answer to one application question') plus a bracketed scope tag [PREPARE - changes local state only; performs NO external action] that separates it from the sibling prepare/save tools. It also names the state transition target (READY_TO_SUBMIT) so the agent knows what this call accomplishes.

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

Usage Guidelines5/5

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

Explicitly scopes when to use it ('one application question that could not be established from their profile/CV/config'), states a hard prohibition ('Never invent the answer yourself: only call this with an answer the user actually gave'), and routes to the verification sibling ('check with linkedin_preview_application').

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