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Openweather

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 5,801 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

A4.4/5.0
Behavior5/5

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

The description discloses the extra LLM call cost, the evidence-only extraction behavior, and the explicit refusal reasons. It aligns with the readOnly and idempotent annotations without contradiction.

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

Conciseness3/5

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

The description is informative but noticeably verbose and repetitive, repeating the same routing and preference details. It could be tightened while preserving the key use-case and cost information.

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

Completeness4/5

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

The description covers return shape, refusal reasons, cost, and use cases, which is strong given the absence of an output schema. It lacks only minor details like example formatting, but overall it is sufficiently complete.

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% and includes the alias information for question, so the baseline applies. The main description does not add additional per-parameter meaning beyond what the schema already documents.

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 clearly states the tool is a hallucination-resistant answer mode for high-stakes reads, extracting answers only from tool results. It also distinguishes itself from the sibling ask_pipeworx by emphasizing grounded, evidence-backed responses.

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?

Explicit guidance says to use this tool whenever answers will be quoted, cited, or acted on, and to prefer ask_pipeworx for casual lookups. This gives clear when-to-use and when-not-to-use direction.

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

B3.4/5.0
Disambiguation2/5

Several tools are near-duplicates or heavily overlapping: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and bet_research all route natural-language queries to structured data, with only subtle differences; ai_visibility_check and scan_competitor_ai_presence also overlap significantly. An agent could easily pick the wrong one.

Naming Consistency2/5

No consistent naming pattern: most tools use lowercase snake_case, but some are noun-first (current_weather, entity_profile), some use brand prefixes (ask_pipeworx, polymarket_*), and 'generate_llms_txt' and 'scan_competitor_ai_presence' are verbose and stylistically inconsistent.

Tool Count3/5

30 tools is above the well-scoped range and feels heavy, though not absurd for a broad multi-domain data platform. The count could be reduced by consolidating near-duplicate query/research tools.

Completeness4/5

The set covers a wide range of domains: weather, SEC/financials, drugs, real estate, prediction markets, news, patents, memory, and subscriptions. Some minor gaps exist (e.g., no list/get subscription tool, no weather alerts), but the main functionality is well covered.

Resources