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dpkdhingra91

AI Interview Agents MCP Server

get_screening_results

Read-only

Get ranked candidates for a screening role, sorted best-first by screening score, including skills, fit, and verdict. Identify top candidates to schedule.

Instructions

Read a screening's ranked candidates (the poll for run_screening).

    Returns {roleId, count, role:{name, mustHaveSkills, niceToHaveSkills,
    location, workMode, additionalRequirements}, candidates:[...]}, candidates
    sorted best-first by screeningScore. Each candidate:
    {id, name, email, hasEmail, phone, position, experienceYears, location,
    locationFit ('remote'|'in_region'|'other'|null), screeningScore (0-100),
    screeningVerdict ('strong'|'possible'|'weak'|'reject'), screeningStatus,
    reason, matchedSkills, relatedSkills, missingSkills, status, screenedAt}.

    DONE-SIGNALS (scoring is async): a candidate is finished when
    screeningStatus is 'scored', 'hard_filtered', 'overridden', or 'error',
    and still pending at 'not_screened'. The whole screening is done when
    every row has left 'not_screened' (or screenedCount == candidateCount in
    list_screening_roles). If rows are still 'not_screened', wait and call again.

    To schedule, take the 'id' of each candidate you want and pass them to
    schedule_screened_candidates. Skip rows where hasEmail is false — they
    need a real email first.

    Results are capped at `limit` (default 100, top by score) and each
    'reason' is truncated to keep the response small; raise limit if you
    need more rows.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
role_idYes
Behavior5/5

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

Beyond annotations (readOnlyHint=true, destructiveHint=false), the description explains async scoring behavior, done-signals, result capping at limit, and truncation of 'reason' fields. No contradiction with annotations.

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?

The description is well-structured with bullet points and clear sections, but slightly verbose. Every sentence adds value, though some details (e.g., full candidate fields) could be condensed.

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?

Despite no output schema, the description provides a detailed output structure. It covers async behavior, status checks, and usage context. Missing explicit mention of requiring a prior screening run, but generally complete for a read tool.

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?

With 0% schema description coverage, the description adds meaning for 'limit' (capping and default) and implicitly clarifies 'role_id' through context. It could explicitly state that 'role_id' identifies the screening's role.

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 'Read a screening's ranked candidates' with a specific verb ('read') and resource ('screening's ranked candidates'). It distinguishes from siblings like 'run_screening' and 'schedule_screened_candidates' by focusing on reading 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?

The description provides explicit guidance on when to use the tool: after running a screening, how to check completion via screeningStatus, and when to poll again. It also names an alternative ('list_screening_roles') for checking candidate counts.

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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