Low Risk

scholarfetch_author_candidates

Disambiguate a human author name into ranked identity candidates. Use this before scholarfetch_author_papers when the name is ambiguous and you need a stable candidate_index. If you pass engines, it must include openalex.

Part of the ScholarFetch server.

scholarfetch_author_candidates 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 scholarfetch_author_candidates to retrieve information from ScholarFetch 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 scholarfetch_author_candidates 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": {
    "scholarfetch_author_candidates": {}
  }
}

See the full ScholarFetch policy for all 12 tools.

Get this rule live on your own ScholarFetch 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 scholarfetch_author_candidates 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 scholarfetch_author_candidates 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 scholarfetch_author_candidates tool do? +

Disambiguate a human author name into ranked identity candidates. Use this before scholarfetch_author_papers when the name is ambiguous and you need a stable candidate_index. If you pass engines, it must include openalex.. It is categorised as a Read tool in the ScholarFetch MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on scholarfetch_author_candidates? +

Register the ScholarFetch MCP server in PolicyLayer and add a rule for scholarfetch_author_candidates: 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 ScholarFetch. Nothing to install.

What risk level is scholarfetch_author_candidates? +

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

Can I rate-limit scholarfetch_author_candidates? +

Yes. Add a rate_limit block to the scholarfetch_author_candidates 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 scholarfetch_author_candidates completely? +

Set action: deny in the PolicyLayer policy for scholarfetch_author_candidates. 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 scholarfetch_author_candidates? +

scholarfetch_author_candidates is provided by the ScholarFetch MCP server (https://laibniz-scholarfetch-web.hf.space/mcp/). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every ScholarFetch tool call.

Deterministic rules across all 12 ScholarFetch tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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