Low Risk

bike_points

Santander Cycle docking stations with live availability (bikes, empty docks, total docks in additionalProperties). Pass an optional search to filter by name/area; omit it to list all docking stations.

Part of the Tfl server.

bike_points is read-only, but an agent in a loop can still rack up calls and cost. PolicyLayer caps every call before it runs. Live in minutes.

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AI agents call bike_points to retrieve information from Tfl without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.

Even though bike_points only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.

Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "bike_points": {}
  }
}

See the full Tfl policy for all 26 tools.

Get this rule live on your own Tfl server in minutes. PolicyLayer enforces it on every call, before it runs.

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These attack patterns abuse exactly the kind of access bike_points gives an agent. Each links to the full case and the policy that stops it:

Browse the full MCP Attack Database →

Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so bike_points only ever does what you allow.

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Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.

What does the bike_points tool do? +

Santander Cycle docking stations with live availability (bikes, empty docks, total docks in additionalProperties). Pass an optional search to filter by name/area; omit it to list all docking stations.. It is categorised as a Read tool in the Tfl MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on bike_points? +

Register the Tfl MCP server in PolicyLayer and add a rule for bike_points: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Tfl. Nothing to install.

What risk level is bike_points? +

bike_points is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit bike_points? +

Yes. Add a rate_limit block to the bike_points rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.

How do I block bike_points completely? +

Set action: deny in the PolicyLayer policy for bike_points. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.

What MCP server provides bike_points? +

bike_points is provided by the Tfl MCP server (https://gateway.pipeworx.io/tfl/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Tfl tool call.

Deterministic rules across all 26 Tfl tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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