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Glama

TeamShared

by io.github.xhad

Context Normalize

context_normalize

Strip, clean, and compress a non-teamshared tool output for agent context.

Trims recall-style payloads, shrinks large JSON/logs, and stores originals in CCR when compressed. Prefer letting MCP middleware handle teamshared tools automatically; call this for Shell, Grep, or other harness tools.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
outputYesRaw tool output string (usually JSON).
tool_nameYesName of the tool whose output you are trimming.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden. It discloses that the tool trims, cleans, shrinks, and stores originals in CCR when compressed. This is meaningful context, though the acronym 'CCR' is left unexplained and the compression trigger conditions are not specified.

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?

Three sentences with no redundancy. The core purpose is front-loaded, followed by specific behavioral details and a clear usage rule. Every sentence earns its place.

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?

The tool has only two simple parameters and an output schema, so the description covers most operational needs: what it does, what types of outputs it targets, and when to call it. The main gaps are the unexplained 'CCR' and the somewhat jargon-heavy 'non-teamshared' term.

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?

Schema description coverage is 100%, so the baseline is 3. The description adds general context about the target tool types but does not add parameter-specific meaning beyond what the schema already provides for tool_name and output.

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 opens with a specific verb-resource action: 'Strip, clean, and compress a non-teamshared tool output for agent context.' It distinguishes its target (Shell, Grep, or other harness tools) from teamshared tools, though it does not explicitly differentiate itself from the closely named sibling context_compress.

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

Usage Guidelines5/5

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

The description explicitly states when to use the tool ('call this for Shell, Grep, or other harness tools') and when not to ('Prefer letting MCP middleware handle teamshared tools automatically'). It also describes what kinds of outputs are appropriate, such as recall-style payloads and large JSON/logs.

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

B3.4/5.0
Disambiguation4/5

With 104 tools across many domains (memory, work, projects, files, agents, context, strategic, ontology), the use of clear prefixes (memory_, work_, project_, file_, agent_run_, context_) makes most tools distinct. However, there are some potential confusions between memory_session_* vs memory_state_*, and memory_recall vs memory_think vs memory_assemble_context, though descriptions clarify their specific purposes. Aliases like memory_playbook_get for memory_procedure_get are explicit and reduce ambiguity.

Naming Consistency5/5

Tool names follow a highly consistent pattern: prefix_domain_action (e.g., file_create, work_update, memory_recall, agent_run_start). All use snake_case, with verbs consistently placed after the domain prefix. Even less common tools like account_brief and attention_snapshot fit the overall naming scheme, making the set predictable and easy to navigate.

Tool Count3/5

At 104 tools, this is an exceptionally large surface area, far exceeding the 25+ threshold that feels heavy. However, the server covers an extensive domain (organizational memory, work management, project tracking, file sharing, agent orchestration, and strategic planning), which justifies a large count. Still, the sheer number may overwhelm agents, and some tools could be consolidated (e.g., many memory_session_* and memory_state_* variants).

Completeness4/5

The tool surface is remarkably complete for its stated purpose, covering CRUD operations for files, work items, projects, and memory, plus lifecycle management for agents, sessions, and strategic plans. Minor gaps exist (e.g., no direct memory_item_get by ID, no section removal in projects), but agents can work around these using existing tools like memory_recall or work_create with parent_id. Overall, the set minimizes dead ends.

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