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dpkdhingra91

AI Interview Agents MCP Server

by dpkdhingra91
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

run_screening

Idempotent

Initiates asynchronous CV-vs-JD scoring for a role's candidates. Returns queued status; poll get_screening_results to retrieve scores after background processing.

Instructions

Start CV-vs-JD scoring for a screening's candidates. ASYNC: this returns immediately with {"status": "queued"} and NO scores — the LLM scoring runs in the background.

    To read results, poll get_screening_results(role_id) a few seconds later
    (and again until candidates leave the 'not_screened' status). Do not
    expect scores in this tool's response.

    - Scoring is idempotent: candidates whose CV+JD are unchanged since the
      last run are skipped. Pass force=True to re-score everything (e.g.
      after you have not changed the JD but want a fresh pass).
    - limit: optionally cap how many candidates get scored this run.
    - Uses a cheap model; safe to run on a large roster.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
forceNo
limitNo
role_idYes

Tool Definition Quality

A4.9/5.0
Behavior5/5

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

Adds significant context beyond annotations: async return, background scoring, idempotency with skip, force re-scoring, limit parameter, cheap model, safe for large roster. No contradictions.

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?

Well-formatted with async warning upfront, bullet points for details, no redundant sentences. Each sentence adds value.

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?

Covers async return, polling pattern, idempotency, force, limit, model safety. With no output schema, explains what to expect and how to get results.

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?

Despite 0% schema description coverage, explains all three parameters: force (re-score all), limit (cap candidates), role_id implied as required. Adds practical usage details.

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?

Clear action verb 'Start' with specific resource 'CV-vs-JD scoring for a screening's candidates'. Distinguishes from sibling tools like get_screening_results which reads results.

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 states async behavior, polling pattern with get_screening_results, idempotency with force option, and when to expect scores. No ambiguity.

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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