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job-search-mcp

Save Resume Profile

save_profile

Extract and save a candidate's profile from pasted resume text, including name, skills, target roles, location, and experience to enable personalized job search and scoring.

Instructions

Extract the candidate's profile from their resume text (which the user pastes in chat) and save it. Pull: full name; core technical skills; suggested target roles; preferred/search location; total years of professional experience (integer); and pass the resume text as rawText. The profile is used by find_jobs (target roles + location) and by scoring (years of experience drives the experience penalty), so set yearsOfExperience accurately. Merges with any existing profile.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoCandidate's full name.
skillsNoCore technical skills.
rawTextNoThe full resume text, stored for reference.
targetRolesNoSuggested target job titles.
prefersRemoteNoWhether the candidate prefers remote.
searchLocationNoPreferred location, e.g. 'California' or 'Remote'.
blockedCompaniesNoCompanies to exclude from sourcing.
yearsOfExperienceNoTotal years of professional experience (integer).
Behavior4/5

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

Without annotations, the description covers the core behavior: extraction, saving, merging, and downstream impact. It does not disclose rate limits or auth needs, but the simple save operation is sufficiently transparent for an AI agent.

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 four sentences with no wasted words. The first sentence immediately states the core action, and subsequent sentences add necessary context efficiently.

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?

Given no annotations and no output schema, the description adequately covers purpose, behavior, and linkage to other tools. It could mention what happens with missing fields or the return value, but overall it is sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds meaning beyond the input schema by explaining how yearsOfExperience drives the experience penalty and that the tool merges with existing profiles. It omits two parameters (prefersRemote, blockedCompanies) from the explanation, but schema coverage is high at 100%.

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 tool extracts a candidate's profile from resume text, saves it, and lists the specific fields pulled. It also explains the profile's downstream use by find_jobs and scoring, distinguishing it from sibling 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 explains that the profile is used for job finding and scoring, implying when to use. It mentions merging behavior, but does not explicitly exclude alternative tools or provide when-not-to-use guidance.

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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