Skip to main content
Glama

update_architecture

Update project architecture records after implementing recommendations to log file changes and maintain documentation in sync.

Instructions

Update the project architecture tracking after implementing recommendations.

This tool provides flexible project lookup. You can specify any combination of identifiers, and it will resolve to the matching project.

Priority: project_id > workspace_path > project_name

WHEN TO USE:

  • After implementing architectural recommendations from get_architecture_recommendation()

  • When completing significant structural changes to the codebase

  • To document what was actually built vs. what was recommended

  • To keep the architecture documentation in sync with the code

IMPORTANT: Call this after implementing architectural recommendations to:

  • Log the implementation details

  • Track files created and modified

  • Update architecture history

  • Document any deviations from recommendations

WORKFLOW:

  1. get_architecture_recommendation() - Get guidance

  2. Implement the recommendation

  3. log_change() - Log each file change

  4. update_architecture() - Update architecture tracking (THIS STEP)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idNoProject ID from create_project() (optional if project_name or workspace_path provided)
project_nameNoProject name to look up (alternative to project_id)
workspace_pathNoWorkspace directory path (alternative to project_id)
recommendation_idYesID from get_architecture_recommendation() (required)
actual_files_createdNoList of new files created (optional) - e.g., ["src/auth.ts", "src/middleware/jwt.ts"]
actual_files_modifiedNoList of existing files modified (optional) - e.g., ["src/app.ts", "src/routes.ts"]
implementation_summaryYesBrief summary of what was implemented (required)
Behavior4/5

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

With no annotations provided, the description carries the transparency burden. It describes the tool's actions (logging implementation details, tracking files, updating history, documenting deviations) and the flexible lookup priority. However, it does not disclose error behavior or return values, which would be useful for full transparency.

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 well-structured with clear sections (purpose, when-to-use, important, workflow), but it is somewhat verbose and slightly repetitive (e.g., the purpose is restated in the 'Important' list). Overall, it earns its length by providing substantial guidance.

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 description covers the tool's purpose, workflow, and parameter semantics comprehensively, making it highly usable for an agent. However, without an output schema, a note about return values or success indication would enhance completeness.

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 description coverage is 100%, so the description adds value by explaining the lookup priority (project_id > workspace_path > project_name) and clarifying that recommendation_id is required and ties to get_architecture_recommendation(). This goes beyond the schema's individual field descriptions.

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 clearly states the verb and resource: 'Update the project architecture tracking after implementing recommendations.' It also differentiates from siblings like update_tech_stack and log_change by focusing on architecture tracking and recommendation implementation.

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 includes a dedicated 'WHEN TO USE' section with explicit scenarios and a workflow that references get_architecture_recommendation() and log_change(), clearly guiding the agent on when to invoke this tool versus alternatives.

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/siddiquesahabaj/CoordMCP'

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