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uber_set_access_token

Store Uber access tokens after OAuth authentication to enable AI assistants to book and manage rides through the MCP Uber Server.

Instructions

Set the access token for a user after OAuth callback

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
userIdYesUnique identifier for the user
accessTokenYesUber access token for the user

Implementation Reference

  • Handler for the uber_set_access_token tool: parses input using SetTokenSchema, stores the access token in the userTokens Map for the user, sets it on the UberClient instance, and returns a success message.
    case 'uber_set_access_token': {
      const { userId, accessToken } = SetTokenSchema.parse(args);
      userTokens.set(userId, accessToken);
      uberClient.setAccessToken(accessToken);
      return {
        content: [
          {
            type: 'text',
            text: 'Access token set successfully',
          },
        ],
      };
    }
  • Zod input schema for the uber_set_access_token tool, validating userId and accessToken as strings.
    const SetTokenSchema = z.object({
      userId: z.string().describe('Unique identifier for the user'),
      accessToken: z.string().describe('Uber access token for the user'),
    });
  • src/index.ts:113-117 (registration)
    Registration of the uber_set_access_token tool in the TOOLS array used for ListToolsRequest, including name, description, and derived JSON input schema.
    {
      name: 'uber_set_access_token',
      description: 'Set the access token for a user after OAuth callback',
      inputSchema: zodToJsonSchema(SetTokenSchema),
    },
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions setting an access token, implying a write/mutation operation, but doesn't disclose behavioral traits like whether this overwrites existing tokens, requires specific permissions, or has side effects like rate limits or authentication changes.

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 a single, efficient sentence that front-loads the purpose without unnecessary details. It earns its place by clearly stating the tool's function and context, with zero waste.

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 mutation tool (implied by 'Set'), the description is incomplete. It lacks details on what happens after setting the token, error conditions, or how this integrates with other Uber tools, leaving gaps for an AI agent to understand full behavior.

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 ('userId' and 'accessToken') adequately. The description doesn't add meaning beyond what the schema provides, such as explaining token format or user context, but baseline 3 is appropriate given high schema coverage.

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 ('Set') and resource ('access token for a user'), specifying it occurs 'after OAuth callback'. However, it doesn't distinguish this from sibling tools like 'uber_get_auth_url', which might also relate to authentication flow.

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

Usage Guidelines3/5

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

The description implies usage context ('after OAuth callback'), suggesting when to use this tool, but doesn't explicitly state when not to use it or name alternatives. For example, it doesn't clarify if this is for initial token setup versus token refresh.

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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