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score_skill_assessment

Submit answers and assessment token to get raw score, percentage, proficiency band, gap vs target, and per-question results. Results can chain into goal creation.

Instructions

Score a skill assessment using answers and the assessment_token from generate_skill_assessment.

Returns the raw score, percentage, proficiency band, gap vs target, and per-question results. When the learner falls short of the target band, includes a suggested_goal_seed you can pass to create_goal.

PER-FACET AGGREGATION (since v0.7.0): Response also includes per_facet (one entry per sub-skill with total, correct, raw_pct, classification) and shortlists weak_facets + strong_facets. Aggregation is computed server-side from facets embedded in the signed token, so partners do not write join logic. The facet_coverage field tells you whether facet labels are real ("full"), partially synthetic ("partial"), or entirely difficulty-bucket fallbacks ("difficulty_fallback") -- soften "weak in X" UI framing when coverage is not "full".

CHAINING into create_goal: The shape of this response is purpose-built to drop straight into create_goal's 'assessment' field (as a skill_proficiency v1 variant):

  • band -> achieved_band

  • target_band -> target_band

  • raw_pct, gap_bands -> same names

  • weak_facets, strong_facets -> same names

  • work_item_context comes from your original generate_skill_assessment call Add assessment_type: "skill_proficiency", schema_version: "v1", and assessed_at (ISO 8601 current time) headers.

Scoring is stateless and deterministic: the signed assessment_token contains the answer key. Tampered or expired tokens are rejected. Same request_id returns the same result (idempotent).

TYPED ERRORS (branch on error_code):

  • "token_invalid": signature mismatch or malformed token. Regenerate via generate_skill_assessment.

  • "assessment_expired": token past TTL. Regenerate.

  • "idempotency_conflict": same request_id was used with different answers; pick a new request_id.

Requires the "assessment:score" scope on your org API key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
answersYesLearner's answers (at least one)
request_idYesClient-supplied idempotency key
band_thresholdsNoOptional override of proficiency band ranges (defaults to the thresholds embedded in the token)
assessment_tokenYesThe signed token from generate_skill_assessment

Tool Definition Quality

A4.4/5.0
Behavior5/5

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

With no annotations, the description fully discloses behavior: stateless and deterministic scoring, token contains answer key, tampered/expired tokens are rejected, idempotent via request_id. It also mentions facet aggregation, coverage field, error codes, and required scope.

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 sections (chaining, errors) and front-loads the main purpose. It is detailed but not verbose; every section adds value. A slightly more condensed version could be possible, but current structure is good.

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?

Given the tool's complexity (4 params, nested objects, no output schema), the description covers everything needed: return values, error handling, chaining instructions, facet aggregation, and required scope. It is fully complete.

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

Parameters3/5

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

Schema description coverage is 100%, so baseline is 3. The description adds minimal extra meaning beyond schema: it explains assessment_token's source and chaining, but does not elaborate on parameters beyond what the schema already provides.

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's purpose: scoring a skill assessment using answers and the assessment_token from generate_skill_assessment. It specifies the resource (skill assessment) and action (score), and distinguishes it from siblings like generate_skill_assessment and create_goal.

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 provides explicit usage context: it must follow generate_skill_assessment, explains chaining into create_goal, and describes stateless, deterministic behavior. It does not explicitly exclude alternative tools, but the context is clear enough.

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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