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Glama

Get artifact

get_artifact

Fetch artifact metadata and a private short-lived download URL by artifact_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
artifact_idYesCourseProfiler artifact ID, e.g. art_...

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesArtifact type, e.g. crsprof, usrprof, pdf, json, or source_file.
metadataNo
warningsNo
file_nameNoSuggested file name.
created_atNoISO-8601 creation timestamp.
expires_atNoISO-8601 artifact expiration timestamp.
size_bytesNoArtifact size in bytes.
artifact_idYesCourseProfiler artifact ID.
content_typeNoMIME content type.
download_urlNoPrivate short-lived download URL. Share only with the user.
download_url_expires_atNoISO-8601 download URL expiration timestamp.

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It adds key behavioral info beyond the schema: the URL is 'private' and 'short-lived', which signals expiration and access restrictions. It does not detail error behavior or auth requirements, but the core fetch semantics are well disclosed.

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?

The description is a single concise, information-dense sentence. It front-loads the main action (Fetch) and includes the key qualifiers (private, short-lived) without any wasted words.

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?

For a simple one-parameter fetch tool with an output schema present, the description covers the essential purpose and key behavior. It does not need to explain return values since the output schema will provide that, and the low complexity means no additional context is required.

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?

The single parameter artifact_id is fully described in the schema with an example format (art_...), achieving 100% schema coverage. The description merely repeats 'by artifact_id' and adds no additional semantic detail, so the baseline of 3 is appropriate.

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?

Description uses a specific verb (Fetch) and resource (artifact metadata and private short-lived download URL), clearly scoped by artifact_id. It distinguishes itself from sibling tools like get_artifact_upload_requirements, which target upload requirements, not fetching an artifact.

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 implies when to use this tool: when you have an artifact_id and need its metadata or download URL. It does not explicitly exclude alternatives or name siblings, but the context is clear enough for an agent to select it over get_artifact_upload_requirements or get_job.

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

A4.1/5.0
Disambiguation4/5

Each tool targets a distinct step in the course/race-planning workflow, and descriptions carefully separate concerns like importing, enriching, segmenting, and generating a plan. The main ambiguity is among the multiple get_*_requirements helpers, but their target phases are clear enough to avoid persistent misselection.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (create_, enrich_, export_, generate_, get_, import_, search_, submit_, validate_). There is no mixing of camelCase or inconsistent verb styles, making the API predictable and easy to navigate.

Tool Count4/5

With 16 tools, the server is at the upper edge of the ideal range but each tool serves a distinct purpose in a complex pipeline covering course import, enrichment, segmentation, runner profiling, plan creation, PDF export, validation, and catalog submission. The count feels justified for the domain rather than bloated.

Completeness4/5

The toolset covers the full lifecycle from course discovery/import through enrichment, segmentation, runner profiling, race plan generation, PDF export, and catalog submission. Minor gaps exist, such as lack of artifact list/delete/update tools and no direct race-plan editing, but agents can work around these via get_artifact and get_job.

Resources