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Glama

Eia

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

A4.6/5.0
Behavior5/5

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

Annotations already show readOnly and idempotent, but the description adds substantial behavioral detail: refused responses with discrete refusal reasons, return fields like evidence and confidence, and the extra LLM call cost. No contradiction with annotations.

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 dense and front-loaded with the key distinction, and every sentence carries useful operational information. The return contract and refusal list are somewhat long but justified by the absence of an output schema.

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?

Given no output schema, the description adequately covers return shape, refusal behavior, cost tradeoff, and routing semantics. An agent has enough information to invoke it correctly and distinguish it from siblings like ask_pipeworx and validate_claim.

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 description coverage is 100% for the single question parameter, including aliases, so the schema fully documents the input. The description does not add new parameter-level meaning, but it doesn't need to.

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?

States a specific purpose: a hallucination-resistant answer mode for high-stakes reads, and immediately contrasts itself with ask_pipeworx. It clearly names the resource (Pipeworx routing) and the distinguishing behavior (extracting answers only from tool results).

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?

Explicitly says when to use this tool — when answers will be quoted, cited, or acted on, and facts must not be invented. Also gives a clear when-not: prefer ask_pipeworx for casual lookups because this costs an extra LLM call.

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 occupy nearly identical roles: ask_pipeworx and ask_pipeworx_beta are described as currently identical, ask_pipeworx_grounded and deep_research overlap with the router, and eia_series overlaps with the specialized eia_electricity/eia_ethanol/eia_natural_gas/eia_petroleum tools. The Polymarket opportunity scanners and the two AI-visibility checkers also blur together, making confident tool selection difficult despite detailed descriptions.

Naming Consistency3/5

Most tools follow a snake_case verb-first pattern (remember, recall, forget, resolve_entity, validate_claim), but there are notable deviations: eia_electricity and eia_ethanol are noun-first category names, recent_alerts and recent_changes are adjective-noun, and pipeworx_trending and polymarket_edges are not verb-driven. The eia_ and polymarket_ prefixes add some predictability, so the naming is readable but inconsistent.

Tool Count2/5

At 36 tools, this is well past the heavy threshold and feels like a kitchen-sink aggregation of several separate products rather than one focused server. Many tools could be consolidated: the five eia_* lookups, the multiple ask_pipeworx variants, and the several Polymarket scanners all serve close purposes. A 36-tool surface is too much for an agent to navigate efficiently.

Completeness4/5

Within the major subdomains the set is quite complete: entity research has profile/compare/changes/resolve, memory has remember/recall/forget, subscriptions have subscribe/list/unsubscribe/recent_alerts, and Polymarket analysis has edge discovery, fill-risk, venue-spread, and persistence tracking. Minor gaps exist—no subscription update flow, no dedicated EIA coal/nuclear/renewables series beyond the generic eia_series fallback—but agents can work around them.

Resources