Returns the TunnelMind analyst config bundle. Configures any LLM (Claude, GPT, Gemini, local) to behave as a TunnelMind analyst that knows the data graph, follows the 5-call golden path, and surfaces attestation_tier on every claim. The bundle is signed inline (Ed25519, key_id from /.well-known/r...
Part of the TunnelMind Data API server.
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AI agents call get_analyst_config to retrieve information from TunnelMind Data API 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 get_analyst_config 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.
{
"version": "1",
"default": "deny",
"tools": {
"get_analyst_config": {}
}
}See the full TunnelMind Data API policy for all 54 tools.
These attack patterns abuse exactly the kind of access get_analyst_config gives an agent. Each links to the full case and the policy that stops it:
Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.
Returns the TunnelMind analyst config bundle. Configures any LLM (Claude, GPT, Gemini, local) to behave as a TunnelMind analyst that knows the data graph, follows the 5-call golden path, and surfaces attestation_tier on every claim. The bundle is signed inline (Ed25519, key_id from /.well-known/receipt-signing-key.json). Add ?receipt=true to wrap the response in a Receipt v1.0 envelope for end-to-end audit. Use this tool when: - You want to configure a new LLM runtime to act as a TunnelMind analyst - You want to verify the system prompt you're running matches what TunnelMind serves - You're building a BYOM (bring-your-own-model) deployment and need the canonical config Do NOT use this tool when: - You want to call individual TunnelMind data tools — use the tools directly - You want to verify a specific receipt — use check_receipt_revoked or @tunnelmindai/receipt-verify Inputs (all optional): - surface (query): "data" (default, full surface), "scry", or "sigil" - version (query): pin a specific bundle version (e.g. "1.0.0" or "1" for latest 1.x.y) - receipt (query): "true" to wrap the response in a signed Receipt v1.0 envelope Content negotiation (via Accept header): - application/json (default) — full bundle JSON - text/markdown — system prompt only (Anthropic flavor) - application/vnd.anthropic.config+json — Anthropic-shaped subset - application/vnd.openai.config+json — OpenAI-shaped subset Returns: - version, schema, issuer, surface, surface_label - system_prompts.{anthropic,openai,generic} — three encodings of the same semantic prompt - tools.surface_subset — array of operationIds for this surface (null = all) - response_format — JSON Schema the analyst's verdicts must conform to - attestation_tiers — the 4-tier vocabulary (self_asserted → silicon_root) - graph_state — live corpus counts at serve time - references — URLs to the rest of the open-protocol layer - bundle_signature — inline Ed25519 signature for offline verification - pin_recommended — stable supply-chain identifier (survives hourly graph_state updates) Headers: X-Bundle-Version, X-Pin-Recommended, ETag, X-RateLimit-*. Cost: - Free, anonymous-accessible. Rate-limited on a SEPARATE counter from data-API calls (cfg:ip:<ip> identity) so a config refetch loop can't burn your data quota. Latency: - Typical <100ms (cached); cold fetch <500ms (live Supabase counts).. It is categorised as a Read tool in the TunnelMind Data API MCP Server, which means it retrieves data without modifying state.
Register the TunnelMind Data API MCP server in PolicyLayer and add a rule for get_analyst_config: 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 TunnelMind Data API. Nothing to install.
get_analyst_config is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the get_analyst_config 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.
Set action: deny in the PolicyLayer policy for get_analyst_config. 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.
get_analyst_config is provided by the TunnelMind Data API MCP server (https://mcp-data.tunnelmind.ai/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 54 TunnelMind Data API tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
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