Parses resume text and stores extracted career facts—education, employment, skills, certifications, achievements—for persistent career coaching and analysis.
Parse and store career facts extracted from resume text. Claude should extract structured facts before calling this.
| Name | Required | Description | Default |
|---|---|---|---|
| raw_text | Yes | Full resume text content | |
| extracted_facts | Yes | Structured facts extracted from the resume |
Changes observed during successful MCP inspections.
v1.0.0Does 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 implies a mutation ('store') and mentions a required pre-processing step, but it does not disclose side effects, duplicate handling, idempotency, authorization needs, or failure behavior. For a write operation, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with no filler. The primary purpose is front-loaded, and the prerequisite is clearly stated in the second sentence. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple in terms of parameters and schema coverage, but there is no output schema and no annotations. The description covers the core operation and prerequisite but omits return value, error conditions, and storage semantics, leaving the agent under-informed for a state-changing tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents both raw_text and extracted_facts. The description adds no new parameter-level meaning beyond referencing 'structured facts', which aligns with the schema's extracted_facts property.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: 'Parse and store career facts extracted from resume text.' It is clearly resume-specific and distinguishable from sibling ingest tools such as atlas_ingest_linkedin, though it does not explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives an explicit prerequisite: 'Claude should extract structured facts before calling this.' This tells the agent what must happen before invocation, which is useful. However, it does not mention when not to use the tool or how it compares to similar tools like atlas_add_career_fact.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.