Skip to main content
Glama

get_image

Retrieve an embedded image from a 3GPP specification as base64-encoded data for direct viewing. Specify a spec ID and image filename to get the visual content, with optional version control.

Instructions

Get an embedded image from a 3GPP specification. Returns the image as base64-encoded data that can be directly viewed by the LLM. Use list_images to discover available images for a spec. Pass version to read a past version's image; the images of an archived version are downloaded on first use, which takes up to a few minutes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesrequired,Image filename (e.g. image1.png)
spec_idYesrequired,Specification ID (e.g. TS 23.501)
versionNoSpecification version to read (e.g. 18.6.0). Also accepts an archive token (i60) or a release selector (Rel-18). Defaults to the version in the database. Use list_versions to see what exists.
Behavior5/5

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

With no annotations provided, the description carries full burden and excels: it states the output is base64-encoded, mentions a latency caveat for archived versions ('up to a few minutes'), and explains the default version behavior. This fully discloses behavioral traits.

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 three sentences, front-loaded with the main action and output, and each sentence adds unique value. Could be slightly more concise by combining the first two sentences, but no superfluous content.

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 no output schema and 3 parameters, the description covers tool purpose, input semantics, latency behavior, and next steps via sibling links. It does not mention what happens if parameters are invalid or if an image doesn't exist, but the overall completeness is high for the context.

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?

Schema coverage is 100% and already explains each parameter well. The description adds value by clarifying `version` accepts both version strings and archive tokens, and defaults to the database version, which goes beyond the schema description.

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 uses specific verbs ('Get an embedded image') and resource ('3GPP specification'), and clearly distinguishes this tool from siblings like `list_images` by stating its function and output format.

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 explicitly points to `list_images` for discovering available images and `list_versions` for version discovery, providing clear guidance on when to use alternative tools. However, it does not mention when not to use this tool (e.g., for non-image content).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/higebu/3gpp-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server