Skip to main content
Glama

get_projects

Retrieve all projects with basic details like ID, name, timestamps, and archived status from Productive.io to access essential project data efficiently.

Instructions

Get all projects with basic information.

Returns project data including:

  • Project ID, name, and number

  • Creation and last activity timestamps

  • Archived status (if applicable)

  • Webapp URL for direct access

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • server.py:188-198 (registration)
    MCP tool registration for 'get_projects'. Thin wrapper that delegates to tools.get_projects(ctx). Defines input schema (none) and output docstring.
    @mcp.tool
    async def get_projects(ctx: Context) -> Dict[str, Any]:
        """Get all projects with basic information.
    
        Returns project data including:
        - Project ID, name, and number
        - Creation and last activity timestamps
        - Archived status (if applicable)
        - Webapp URL for direct access
        """
        return await tools.get_projects(ctx)
  • tools.py:27-48 (handler)
    Core handler logic: logs progress, calls client.get_projects with sort param, filters response using filter_response, handles ProductiveAPIError and general exceptions using shared error handler.
    async def get_projects(ctx: Context) -> ToolResult:
        """Fetch projects and post-process response for LLM safety.
    
        Developer notes:
        - Wraps client.get_projects(); sorts by most recent activity first.
        - Applies utils.filter_response to strip noise and add webapp_url.
        - Raises ProductiveAPIError on API failure; errors are logged via ctx.
        """
        try:
            await ctx.info("Fetching all projects")
            params = {"sort": "-last_activity_at"}
            result = await client.get_projects(params=params)
            await ctx.info("Successfully retrieved projects")
            filtered = filter_response(result)
    
            return filtered
    
        except ProductiveAPIError as e:
            await _handle_productive_api_error(ctx, e, "projects")
        except Exception as e:
            await ctx.error(f"Unexpected error fetching projects: {str(e)}")
            raise e
  • Productive API client method that performs the HTTP GET request to /projects endpoint with optional params.
    async def get_projects(self, params: Optional[dict] = None) -> Dict[str, Any]:
        """Get all projects"""
        return await self._request("GET", "/projects", params=params)
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns 'all projects' and lists the data fields included, which adds useful context about scope and output. However, it lacks details on behavioral traits such as pagination, rate limits, permissions, or error handling, which are important for a read operation.

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?

The description is front-loaded with the core purpose in the first sentence, followed by a bulleted list of return data that adds necessary detail without redundancy. Every sentence earns its place, and the structure is clear and efficient, with no wasted words.

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 tool's low complexity (0 parameters, read-only operation) and the presence of an output schema, the description is reasonably complete. It covers the scope ('all projects') and output fields, which complements the structured data. However, it could improve by addressing usage guidelines or behavioral aspects like data freshness or limits.

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?

The input schema has 0 parameters with 100% coverage, so the schema fully documents the lack of inputs. The description doesn't need to add parameter details, and it correctly implies no filtering or arguments by stating 'Get all projects.' This aligns well with the schema, earning a baseline score above 3 due to the zero-parameter context.

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 clearly states the tool's purpose: 'Get all projects with basic information.' It specifies the verb ('Get') and resource ('projects'), and distinguishes it from siblings like get_pages or get_tasks by focusing on projects. However, it doesn't explicitly differentiate from potential similar tools (e.g., 'get_filtered_projects'), which keeps it from a perfect score.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, exclusions, or comparisons with sibling tools like quick_search or get_recent_activity, leaving the agent to infer usage based on context alone.

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/druellan/Productive-GET-MCP'

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