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artifact_save

Read-only

Approve/save an artifact and mark tenant-scoped learning-loop eligibility. Does not publish externally.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
approvedNo
draft_idYes
tenant_idYes
reuse_tagsNo
learning_scopeNo
idempotency_keyNo
approval_event_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

C2.8/5.0
Behavior1/5

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

The description says 'Approve/save' and 'mark eligibility', which are write-like side effects, but the annotations declare readOnlyHint=true. This is a direct contradiction that misleads agents about whether the tool mutates state. The added 'Does not publish externally' is useful but cannot compensate for the fundamental inconsistency.

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?

Two short sentences deliver the core purpose and a key behavioral boundary with no filler. Every clause earns its place and the most important action is front-loaded.

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

Completeness2/5

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

Despite an output schema being present, the tool has seven parameters, no parameter documentation, and no reliable behavioral annotations. The description clarifies intent but omits side effects, parameter semantics, prerequisites, and how the learning-loop eligibility is recorded, making it insufficient for confident invocation.

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

Parameters2/5

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

Schema description coverage is 0%, so the description carries the burden of explaining the seven parameters, but it explains almost none. It vaguely maps to tenant_id, draft_id, approved, and learning_scope through phrases like 'tenant-scoped' and 'learning-loop eligibility,' but leaves reuse_tags, idempotency_key, and approval_event_id completely unexplained.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a clear action ('Approve/save an artifact') and a specific outcome ('mark tenant-scoped learning-loop eligibility'), which distinguishes it from artifact_generate, artifact_export, and artifact_refine. The 'Does not publish externally' phrase adds a boundary that further clarifies its role, though 'save' is slightly generic.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies this tool is for internal, tenant-scoped save/approval workflows and explicitly says it does not publish externally, giving some usage boundary. However, it does not name sibling alternatives or state when to prefer another tool, so the guidance is largely implicit.

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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TDQS

C2.6/5.0
Disambiguation1/5

Several tools are exact duplicates (talent_scout_my_profile_status and talent_scout_profile_status have identical descriptions), and eight estimator_estimate_* tools share the same generic description with no differentiation. This will cause misselection.

Naming Consistency3/5

Most tools follow a snake_case verb_noun pattern, but there are inconsistencies: the duplicate profile tools have different naming (my_profile vs profile), and `fetch`/`search` are single-word verbs. Predictability is hampered by these deviations.

Tool Count2/5

65 tools is excessive for a coherent set, especially with many tools covering overlapping actions across multiple unrelated domains (AI receptionist, estimator, talent scout, GrowthOS). The count could be trimmed significantly.

Completeness3/5

The tool surface is broad and covers many lifecycle operations (create, read, export, record), but the duplicate tools and identical descriptions for estimator operations make it unclear whether all needed operations are present. Some expected operations like delete/update are missing for certain resources.

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