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ipinfo

by io.github.pipeworx-io

Ask Pipeworx — Grounded

ask_pipeworx_grounded
Read-onlyIdempotent

Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 5,798 across 1517 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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for question.
textNoAlias for question.
inputNoAlias for question.
queryNoAlias for question.
promptNoAlias for question.
questionYesYour question in natural language. Accepts query, q, prompt, text, input as aliases.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.7/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description goes beyond the readOnly/idempotent annotations by disclosing refusal behavior, exact refusal reasons, evidence format, and the fact that the answer is extracted solely from tool results. It also exposes an extra LLM-call cost, which is exactly the kind of behavioral context an agent needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but every sentence earns its place: purpose, routed behavior, return contract, refusal contract, usage guidance, and cost tradeoff. It is front-loaded with the core purpose and then layers necessary detail without fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no output schema, the description provides a full return-shape explanation including success and refusal cases. It also situates the tool relative to ask_pipeworx, covers cost, and states clear usage criteria, so an agent has everything for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%: all six parameters are documented aliases for 'question'. The description reinforces the natural-language intent but does not need to add parameter-level detail because the schema already fully explains the inputs. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific behavior: hallucination-resistant, grounded answer extraction for high-stakes reads. It explicitly contrasts with ask_pipeworx and explains what the tool does differently rather than merely restating the title.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly says when to use this mode ('whenever an answer will be quoted, cited, or acted on') and when not to ('prefer ask_pipeworx for casual lookups'). It also names the sibling alternative and the tradeoff, leaving no ambiguity about selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.6/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer data questions; polymarket_arbitrage, polymarket_edges, and polymarket_edge_tracker all scan prediction markets. Despite long descriptions, an agent could easily select the wrong one, especially the currently identical ask_pipeworx and ask_pipeworx_beta.

Naming Consistency3/5

Mostly snake_case, but with inconsistent patterns: get_my_ip/lookup_ip use verb_noun, entity_profile/recent_changes are noun phrases, remember/recall/forget are bare verbs, and the polymarket_* tools share a brand-prefixed noun style. Readable but not a predictable, uniform convention.

Tool Count2/5

At 33 tools, the server is overloaded, far exceeding the 25-tool threshold for 'too many.' It combines two unrelated identities—the original ipinfo IP lookup and the massive Pipeworx data/research platform—making the toolset heavy and harder for an agent to navigate efficiently.

Completeness4/5

The tool surface provides strong coverage of the data query and research lifecycle: discovery (discover_tools, suggest_questions), entity resolution (resolve_entity), lookup (ask_pipeworx, entity_profile), verification (validate_claim, ask_pipeworx_grounded), and post-processing (search_within, recent_changes). Minor gaps exist—for example, no dedicated 'get SEC filing by accession' tool or single-purpose financials endpoint—but these are workaroundable via the routing tools.

Resources