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Glama

Mychem

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. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Goes beyond annotations by disclosing it extracts only from tool results, returns verbatim evidence, explicit refusal reasons, and costs one extra LLM call. No contradiction with readOnlyHint, openWorldHint, idempotentHint, or destructiveHint.

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 thorough but slightly long, front-loading the main purpose and then detailing behavior, usage, and cost. Every sentence adds value, though the return type spec could be trimmed slightly. Well-structured and easy to parse.

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?

Provides complete context: return shape, refusal reasons, use cases, cost trade-off, and differentiation from ask_pipeworx. Since no output schema exists, the description fulfills that role. Nothing essential is missing 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 covers 100% of parameters, including descriptions for all aliases. The description adds no additional parameter-level meaning beyond confirming natural language input, which is already in the schema. Baseline 3 is appropriate given 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?

Clearly states it is a hallucination-resistant answer mode that extracts answers only from tool results, explicitly distinguishing itself from sibling ask_pipeworx by the extraction step. Specific use case (high-stakes reads) and contrast with casual lookups make its purpose unmistakable.

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 states when to use (quoted, cited, or acted on; must not invent facts) and when not (casual lookups, prefer ask_pipeworx). Names the alternative and gives a cost-based reason (one extra LLM call), 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.

All Mychem toolsInspect
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TDQS

A3.9/5.0
Disambiguation2/5

The set contains several near-duplicate entry points (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) plus a dense family of polymarket_* and chemical lookup tools whose boundaries are subtle. An agent can easily pick the wrong router variant, edge-scanning tool, or chemical search tool.

Naming Consistency3/5

Snake_case is used throughout, but naming style is mixed: some tools are verb-first (query, resolve_entity, validate_claim), some are noun/domain-first (entity_profile, recent_changes, polymarket_arbitrage), and the ask_pipeworx variants use suffixes instead of a consistent verb pattern. It is readable, but there is no predictable convention beyond snake_case.

Tool Count2/5

34 tools is well past the comfortable range, and the count is especially hard to justify because they span unrelated domains: chemistry lookups, a Pipeworx data gateway, prediction-market analytics, memory, subscriptions, AI-visibility checks, and npm dependency scanning. Many of these have no obvious connection to the 'Mychem' name or to each other.

Completeness4/5

Within each contained workflow the surface is fairly complete: chemistry has search/fetch/metadata, Pipeworx has lookup/research/discovery/validation, subscriptions and memory both have lifecycle coverage, and the Polymarket family covers research, edge detection, persistence, and fill risk. Some minor gaps exist (no general web retrieval or execution/trading action), but agents can work around them.

Resources