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Glama

Get Context

get_context
Read-onlyIdempotent

Get a token-efficient context pack with trust and execution guardrails for an agent.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toolYes
distroNo
localeNo

Schema Changelog

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

  1. First observed

TDQS

C2.3/5.0
Behavior2/5

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

Annotations already declare the tool read-only, idempotent, and non-destructive. The description adds 'trust and execution guardrails' and 'token-efficient', which hint at behavioral traits, but these terms are vague and not elaborated. The description does not meaningfully increase transparency beyond the annotations.

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 a single sentence, front-loaded with the main action 'Get a token-efficient context pack'. It is concise and free of filler, though its vagueness limits the value of its brevity.

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?

For a tool with three parameters and no output schema, the description is severely under-specified. It does not explain what the context pack contains, how the parameters affect the result, or what the response looks like. The annotations cover only the safety profile, not the tool's behavior.

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

Parameters1/5

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

Schema coverage is 0%, so the description must compensate for missing parameter information. However, it mentions none of the three parameters (tool, distro, locale) and gives no hints about their purpose or formats. This leaves the agent with no semantic understanding of how to populate the call.

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

Purpose3/5

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

The description states the tool gets a 'token-efficient context pack' for an agent, which is a specific resource, but the resource is vague and not differentiated from sibling tools that also provide focused information. The verb 'get' is clear, but the object 'context pack' lacks concreteness, leaving the tool's exact purpose unclear.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives. The description only mentions 'for an agent' as an audience, but does not explain circumstances, prerequisites, or exclusions. Without any comparison to sibling tools, the agent has no basis for choosing this tool.

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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Glama MCP Gateway

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TDQS

B3.2/5.0
Disambiguation3/5

The tool set covers many closely related operations: three 'compare_*' tools, four command-diagnostic tools, and several lookup tools that can overlap. Descriptions help but boundaries are not always obvious (e.g., identify_binary vs query_file_provides, lint_command vs suggest_fix), so an agent may select the wrong tool for a task.

Naming Consistency5/5

All tool names follow a consistent verb_noun (or verb_noun_noun) pattern with snake_case. Verbs such as get, search, compare, and diagnose are used predictably, making the set's structure easy to learn.

Tool Count3/5

22 tools is more than the typical well-scoped set (3-15), and the server covers a broad but unified domain. While each tool serves a distinct purpose, the granularity is slightly heavy—some comparison and diagnostic tools could potentially be merged.

Completeness4/5

The tool surface covers core intelligence workflows: searching, fetching details, comparing, resolving installs, migration planning, lifecycle checks, and vulnerability lookup. Minor gaps exist such as a direct 'list_distributions' tool, but agents can work around these using existing tools.

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