Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 4,676 across 1213 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbatim quote), confide...
AI agents call ask_pipeworx_grounded to retrieve information from Mcp Gsa Perdiem without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
q | string | — | Alias for question. |
text | string | — | Alias for question. |
input | string | — | Alias for question. |
query | string | — | Alias for question. |
prompt | string | — | Alias for question. |
question | string | Yes | Your question in natural language. Accepts query, q, prompt, text, input as aliases. |
Parameters from the server's own tool schema.
This tool retrieves and queries data from a pool of 4,676 tools across 1213 sources, then extracts answers without side effects. It is purely a read operation that fetches and presents information. The emphasis on hallucination resistance and refusal mechanisms indicates it is designed for safe, non-destructive information retrieval.
From the tool's definition Described as 'Hallucination-resistant answer mode for high-stakes reads' that 'EXTRACTS the answer using ONLY what the tool result contains' and 'fetches the data'.
Risk signalsAccepts freeform code/query input (query)
Attacks that exploit this kind of access
Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 4,676 across 1213 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbatim quote), confidence, source, fetched_at, refusal_reason:null} on success, OR an explicit refusal {answer:null, refusal_reason:"not_in_source"|"no_tool_match"|"tool_error"|"data_truncated"|"llm_error"} when the data doesn't directly answer. Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements). Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups. It is categorised as a Read tool in the Mcp Gsa Perdiem MCP Server, which means it retrieves data without modifying state.
ask_pipeworx_grounded accepts 6 parameters: q, text, input, query, prompt, question. Required: question. The full parameter table on this page comes from the server's own tool schema.
Register the Mcp Gsa Perdiem MCP server in PolicyLayer and add a rule for ask_pipeworx_grounded: 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 Gsa Perdiem. Nothing to install.
ask_pipeworx_grounded 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 ask_pipeworx_grounded 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 ask_pipeworx_grounded. 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.
ask_pipeworx_grounded is provided by the Mcp Gsa Perdiem MCP server (https://gateway.pipeworx.io/gsa-perdiem/mcp). 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.
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