Skip to main content
Glama

project_list

List and filter projects within an organization to track progress and manage hierarchical structures for technical project management.

Instructions

PROJECT MANAGEMENT: List projects in an organization.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
org_idNoFilter by organization ID (case-insensitive)

Implementation Reference

  • Registers the 'project_list' tool with the MCP server, defining its name, description, and input schema (optional org_id filter).
    Tool(
        name="project_list",
        description="PROJECT MANAGEMENT: List projects in an organization.",
        inputSchema={
            "type": "object",
            "properties": {
                "org_id": {"type": "string", "description": "Filter by organization ID (case-insensitive)"}
            },
        },
    ),
  • Tool handler in _handle_tool: retrieves projects using db.list_projects (filtered by org_id if provided), serializes to JSON, and returns.
    if name == "project_list":
        projects = db.list_projects(args.get("org_id"))
        return _json([p.model_dump() for p in projects])
  • Core implementation in TrackerDB: queries projects table (optionally filtered case-insensitively by org_id), maps rows to Project models.
    def list_projects(self, org_id: str | None = None) -> list[Project]:
        if org_id:
            org_id = self._normalize_id(org_id)
            rows = self.conn.execute(
                "SELECT * FROM projects WHERE LOWER(org_id) = ? ORDER BY name", (org_id,)
            ).fetchall()
        else:
            rows = self.conn.execute("SELECT * FROM projects ORDER BY name").fetchall()
        return [
            Project(
                id=r["id"],
                org_id=r["org_id"],
                name=r["name"],
                repo_path=r["repo_path"],
                description=r["description"],
                created_at=datetime.fromisoformat(r["created_at"]),
            )
            for r in rows
        ]
  • Pydantic model for Project used in output serialization (model_dump() produces the JSON response structure).
    class Project(BaseModel):
        id: str
        org_id: str
        name: str
        repo_path: str | None = None
        description: str | None = None
        created_at: datetime
Behavior2/5

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

With no annotations provided, the description carries full burden but only states the basic action ('List projects'). It doesn't disclose behavioral traits such as pagination, sorting, default limits, authentication requirements, rate limits, or what happens if org_id is omitted. This leaves significant gaps for a tool that likely returns multiple items.

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 extremely concise with just one sentence, front-loaded with the domain context and core action. There is zero wasted verbiage, making it efficient for an AI agent to parse, though this conciseness comes at the cost of completeness.

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?

Given no annotations, no output schema, and a simple input schema, the description is incomplete. It lacks details on return format (e.g., list structure, fields), error conditions, or behavioral expectations like pagination. For a list tool with potential complexity in output, this is inadequate despite the simple parameter set.

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%, with the schema fully documenting the org_id parameter. The description adds minimal value beyond the schema by implying the org_id context ('in an organization'), but doesn't provide additional syntax, format details, or usage examples. Baseline 3 is appropriate as the schema does the heavy lifting.

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 verb ('List') and resource ('projects'), with the context 'PROJECT MANAGEMENT' providing domain specificity. It distinguishes from siblings like project_create (creation) and org_list (different resource), though it doesn't explicitly differentiate from other list tools like note_list or task_list.

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 like org_list (for organizations) or other list tools. It mentions 'in an organization' which hints at the org_id parameter context, but offers no explicit when/when-not instructions or named alternatives for similar operations.

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/urjitbhatia/tpm-mcp'

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