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climate

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 5,767 across 1506 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

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnly/openWorld/idempotent hints, but the description adds significant behavioral details: it returns evidence with verbatim quotes, provides structured refusal reasons for five distinct failure modes, and discloses the extra LLM call cost. No contradiction with annotations exists.

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

Conciseness4/5

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

The description is information-dense but every sentence contributes something necessary: purpose, routing, return behavior, usage guidance, and cost tradeoff. It is front-loaded with the core 'hallucination-resistant' concept, though the inclusion of exact source counts (5,767 tools, 1506 sources) is slightly extraneous.

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?

With no output schema, the description fully covers return format, failure modes, and refusal reasons. It also explains the tool's relationship to ask_pipeworx and provides clear usage context. Nothing essential is missing for an agent to call this tool correctly.

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%, with each parameter documented as an alias for 'question'. The description does not add parameter-level semantics, but the schema already fully describes the single meaningful argument. Baseline of 3 is appropriate given the high schema coverage.

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 identifies this as a 'hallucination-resistant answer mode' that extracts answers using only tool result content, differentiating it from the standard ask_pipeworx. Specific behavior and resource are named, and the distinction from siblings is explicit.

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?

The description explicitly states when to use this tool ('whenever an answer will be quoted, cited, or acted on') and when not to ('prefer ask_pipeworx for casual lookups'). It also names the alternative tool and notes the cost tradeoff, giving clear decision criteria.

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

A3.9/5.0
Disambiguation2/5

Multiple query-router tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and bet_research all serve as entry points into the same underlying data, with ask_pipeworx_beta explicitly described as currently identical to ask_pipeworx. Prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk) also have fuzzy boundaries that an agent could easily mis-select.

Naming Consistency3/5

Most tools follow a readable lower_snake_case verb_noun pattern (compare_entities, get_climate_projection, resolve_entity), but conventions are mixed: pipeworx_feedback, pipeworx_trending, recent_alerts, and recent_changes are noun-first/non-imperative, and the ask_pipeworx family uses a verb-plus-variant-suffix style. The pattern is predictable enough to navigate but not consistent.

Tool Count2/5

33 tools is heavy for a single MCP server, and the server name 'climate' does not match the broad data-research, prediction-market, memory, subscription, and web-utility scope actually covered. Several tools could be consolidated (ask_pipeworx variants, discover_tools/suggest_questions, multiple polymarket scanners), which would reduce cognitive load without losing capability.

Completeness4/5

As a general data-access and research surface, the tool set is quite complete: it covers lookup, grounded verification, deep research, entity resolution, comparisons, recent changes, memory, subscriptions, alerts, feedback, and tool discovery. Minor gaps exist — the climate-specific coverage is limited to projections and model comparison despite the server name, and there is no direct tool to page through the full catalog — but for its inferred broad purpose there are no major dead ends.

Resources