Skip to main content
Glama

Get Career Context

get_career_context
Read-onlyIdempotent

Read approved resume points and beyond-resume memory to ground LinkedIn posts and outreach drafts in recorded career facts.

Instructions

Read the full composed career context from the Career KB: resume (the approved-points resume view assembled from profile + non-archived entities) and memory (the beyond-the-resume string: identity facts and entity notes that don't belong on a resume). Intended as grounding for writing about the user's career (LinkedIn posts, outreach drafts), optionally with get_base_resume for a specific resume's voice. resume carries approved points only; memory also includes the profile summary and notes and the non-archived entities' notes. It never invents facts, metrics, employers, or dates; anything absent from it (and from a fetched base resume) is not part of the user's recorded career. It carries no IDs: kb_list_entities / kb_get_entity / kb_list_points return the IDs needed to correct anything.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.11.1

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive/openWorld=false, yet the description adds real behavioral context beyond them: it never invents facts, metrics, employers or dates, absence from the output means the fact is not recorded, and it deliberately carries no IDs (routing to kb_list_entities / kb_get_entity / kb_list_points). This is substantive disclosure layered on top of the safety profile.

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?

Front-loaded with what is read and the two output sections before the usage and caveat sentences, so an agent can stop early. It is dense and slightly long, but each sentence (composition, grounding use, no-invention guarantee, ID absence) carries information rather than filler.

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

Completeness5/5

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

With no output schema, the description must explain the return shape, and it does: `resume` is approved points only while `memory` adds the profile summary, notes, and notes from non-archived entities. Combined with the grounding contract and ID guidance, an agent has everything needed to call and interpret this correctly.

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 tool takes zero parameters, so the baseline is 4 and there is nothing for the description to disambiguate. No misleading parameter hints are present.

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?

Specific verb+resource (reads the composed career context from the Career KB) with the two returned sections, `resume` and `memory`, explicitly defined. It distinguishes itself from the kb_* siblings by naming them as the ID-bearing tools it is not, and from get_base_resume by framing that as an optional companion.

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?

States the intended use clearly: grounding for writing about the user's career (LinkedIn posts, outreach drafts), optionally paired with get_base_resume for a specific resume's voice. It names the correct siblings for ID correction, but gives no explicit when-not-to-use condition or exclusion.

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

Deploy Server

Other Tools