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valem

by io.github.vlad-public-code

Upload blob

upload_blob
Idempotent

Store binary content (base64-encoded in 'data') in the content-addressed blob store and get back a BlobRef {$blobId, $mediaType, $bytes} to embed in a model's binary field. Storage is content-addressed (SHA-256), so uploading identical bytes returns the same $blobId.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesThe blob content, base64-encoded.
mediaTypeNoOptional media type (default application/octet-stream).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
$bytesNoSize in bytes.
$blobIdNoContent-addressed id (sha256:...).
$mediaTypeNoThe stored media type.

Schema Changelog

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

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations mark readOnlyHint false and destructiveHint false, and the description explicitly describes content-addressed storage, reinforcing idempotency (annotated idempotentHint true). It does not detail error conditions, size limits, or the exact persistence semantics, but the given annotations and text give a reasonable picture of side effects.

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?

Two sentences cover purpose, storage behavior, and return type. No redundant details or verbose explanations; the description is direct and information-dense without being overloaded.

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?

Given the output schema is present (though not shown), the description maps the response to a struct. Since this is a storage operation (not destructive per annotations) and idempotency is highlighted, the context is sufficient for basic usage. It doesn't mention whether blobs can be deleted or any permission requirements, but the provided context (idempotent, non-destructive) partially covers that.

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?

Both parameters are described in the schema: 'data' as base64-encoded blob content and 'mediaType' with an optional note and default. The description reiterates the meaning of 'data' and mentions the return BlobRef with fields. This is sufficient for an agent to know what to provide.

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 clearly states the verb ('upload'), the resource ('blob store'), the input format (base64 in 'data'), and the purpose (getting a BlobRef to embed in a model's binary field). Even states the content-addressing behavior, making it unambiguous what the operation does.

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?

Explains when to use (to embed binary content in a model's binary field) and hints at idempotent behavior (identical bytes yield same blobId). However, it does not contrast with the sibling `download_blob` tool or specify any prerequisites or conditions that would make this tool inappropriate, leaving some usage guidance 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

A3.9/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but get_audit explicitly subsumes get_history and explain, and get_state with paths overlaps get_field, creating minor selection ambiguity. The detailed descriptions help, but an agent could still reach for the wrong getter.

Naming Consistency4/5

Naming is overwhelmingly consistent: snake_case with verb_noun structure and coherent get_/create_/delete_ clusters. Minor deviations like bare verbs (mutate, explain, restore, snapshot) and eval instead of evaluate prevent a perfect score.

Tool Count3/5

27 tools is above the comfortable range and feels heavy, especially with several overlapping audit/state getters that could be consolidated. That said, the domain is broad enough that the count is defensible, so it is heavy but not chaotic.

Completeness4/5

The tool set covers the full model lifecycle well: create, validate, test, mutate, evolve, read, delete, plus snapshot/restore, audit, blobs, views, library, and expression evaluation. Minor gaps like explicit export/import or separate view-management tools are workable around.

Resources