ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1213 STRUCTURED data sources (SEC filings, FRED/BLS economi...
AI agents call deep_research to retrieve information from Mcp Celestrak without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
depth | string | — | How many facets to research in parallel: quick=3, standard=5 (default), thorough=8 (paid plans). |
question | string | Yes | The research question, in natural language. Broad/multi-part is fine — decomposition is the point. |
Parameters from the server's own tool schema.
Despite the name 'deep_research', this is fundamentally a Read operation: it queries multiple data sources in parallel and returns findings without side effects. The tool is restricted to signed-in users and performs structured data retrieval only. No write, execute, destructive, or financial operations are indicated.
From the tool's definition "Grounded multi-source research" across "STRUCTURED data sources" (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records) — tool queries and retrieves existing data without modification.
Attacks that exploit this kind of access
ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1213 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 4,676 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Expect 15-60s. It is categorised as a Read tool in the Mcp Celestrak MCP Server, which means it retrieves data without modifying state.
deep_research accepts 2 parameters: depth, question. Required: question. The full parameter table on this page comes from the server's own tool schema.
Register the Mcp Celestrak MCP server in PolicyLayer and add a rule for deep_research: 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 Mcp Celestrak. Nothing to install.
deep_research 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 deep_research 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 deep_research. 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.
deep_research is provided by the Mcp Celestrak MCP server (pipeworx-io/mcp-celestrak). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
Teams ship this data inside their own products. See what a licence covers →