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update_project_column

Modify the name of an existing project column in GitHub by specifying its unique identifier and the desired new name.

Instructions

Update an existing project column

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
column_idYesThe unique identifier of the column
nameYesNew name of the column

Implementation Reference

  • The core handler function that executes the tool logic by making a PATCH request to the GitHub API to update the project column's name.
    export async function updateProjectColumn(columnId: number, name: string) {
        try {
            const url = `https://api.github.com/projects/columns/${columnId}`;
    
            const response = await githubRequest(url, {
                method: 'PATCH',
                body: {
                    name: name
                },
                headers: {
                    'Accept': 'application/vnd.github.inertia-preview+json'
                }
            });
    
            return response;
        } catch (error) {
            if (error instanceof GitHubError) {
                throw error;
            }
    
            throw new GitHubError(`Failed to update project column: ${(error as Error).message}`, 500, { error: (error as Error).message });
        }
    }
  • Zod schema defining the input parameters for the update_project_column tool: column_id (number) and name (string).
    export const UpdateProjectColumnSchema = z.object({
        column_id: z.number().describe("The unique identifier of the column"),
        name: z.string().describe("New name of the column"),
    });
  • index.ts:236-239 (registration)
    Tool registration in the ListTools response, defining the tool's name, description, and input schema.
      name: "update_project_column",
      description: "Update an existing project column",
      inputSchema: zodToJsonSchema(projects.UpdateProjectColumnSchema),
    },
  • index.ts:655-664 (registration)
    Dispatch handler in the switch statement that parses arguments, calls the projects.updateProjectColumn function, and returns the result.
    case "update_project_column": {
      const args = projects.UpdateProjectColumnSchema.parse(request.params.arguments);
      const result = await projects.updateProjectColumn(
        args.column_id,
        args.name
      );
      return {
        content: [{ type: "text", text: JSON.stringify(result, null, 2) }],
      };
    }
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. While 'Update' implies a mutation operation, the description doesn't disclose important behavioral traits like required permissions, whether this operation is reversible, what happens to existing data in the column, error conditions, or rate limits. For a mutation tool with zero annotation coverage, this is a significant gap.

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 - a single sentence with zero wasted words. It's front-loaded with the essential information (update operation on project column) and doesn't include any unnecessary elaboration. This is an example of efficient communication.

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 mutation tool with no annotations and no output schema, the description is insufficiently complete. It doesn't address behavioral aspects (permissions, side effects, error handling), doesn't differentiate from similar sibling tools, and provides no information about what the tool returns. Given the complexity of project management operations and the lack of structured metadata, more context is needed.

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 schema already documents both parameters (column_id and name) with clear descriptions. The description doesn't add any parameter semantics beyond what's in the schema - it doesn't explain format requirements, constraints, or provide examples. With complete schema coverage, the baseline score of 3 is appropriate.

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 action ('Update') and target resource ('an existing project column'), providing specific verb+resource pairing. However, it doesn't distinguish this tool from similar sibling tools like 'update_project', 'update_project_v2', or 'update_project_v2_item_field', which all involve updating project-related entities.

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. There are multiple update-related sibling tools (update_project, update_project_v2, update_project_v2_item_field, update_issue), but the description doesn't clarify when this specific column update tool is appropriate versus those other update operations.

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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