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Glama

Osha

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 5,743 across 1500 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.

TDQS

A4.7/5.0
Behavior5/5

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

The description goes well beyond the annotations, detailing exact success and refusal return shapes, the specific refusal reasons, and the guarantee that the answer uses only tool-result content. This is critical for an agent deciding whether to trust and propagate the answer.

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: core behavior, return contract, refusal contract, usage guidance, and cost trade-off are all included without filler. The most important differentiator is front-loaded.

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 grounded Q&A tool with no output schema, the description is fully self-contained: it explains what happens on success, what happens when the data is insufficient, and when to prefer the cheaper sibling. Nothing essential for correct invocation is missing.

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 the schema already documents the question parameter and its aliases clearly. The description adds little about parameter semantics, but with complete schema coverage, the baseline of 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 purpose: a hallucination-resistant answer mode that extracts answers solely from tool results. It clearly distinguishes itself from the sibling ask_pipeworx by emphasizing grounded, high-stakes output with evidence and refusal handling.

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 is given: use when answers will be quoted, cited, or acted on, and prefer ask_pipeworx for casual lookups. It also mentions the extra LLM call cost, giving the agent a concrete trade-off for tool 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

A4.1/5.0
Disambiguation3/5

Several tools have similar purposes, such as the four ask_pipeworx variants and multiple prediction market analysis tools (bet_research, polymarket_edges, polymarket_arbitrage, etc.). While descriptions clarify differences, the overlap could cause misselection by an agent, especially with the high number of specialized market tools.

Naming Consistency4/5

All tool names use snake_case, but the pattern is not fully consistent: some start with verbs (ask_pipeworx, bet_research, compare_entities) while others are noun phrases (entity_profile, recent_alerts, osha_search). This minor inconsistency does not severely hinder readability.

Tool Count3/5

33 tools is on the high side, but the server acts as a comprehensive data gateway covering multiple domains (financials, prediction markets, OSHA, etc.) and includes meta-tools (memory, subscriptions, feedback). The count is justified by the breadth, though it borders on being overwhelming.

Completeness4/5

The tool set covers core workflows: data lookup (ask_pipeworx), entity profiles, comparisons, prediction market analysis, and memory management. There are minor gaps, such as no direct SEC filing retrieval tool (handled via ask_pipeworx), but the overall surface is comprehensive for the server's stated purpose.

Resources