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Glama

Schemastore

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 declare readOnly, openWorld, idempotent, and non-destructive. The description goes well beyond this by revealing the exact success response shape, the explicit refusal reasons, the extraction-only-from-source behavior, and the added cost of one LLM call. No contradictions exist.

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 but every major section earns its place: purpose, behavior, return format, refusal reasons, usage guidance, and cost tradeoff. It is front-loaded with the core promise. Slightly long due to the exhaustive refusal list, but that list is directly actionable for agent decision-making.

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 there is no output schema, the description compensates thoroughly by specifying return fields, refusal semantics, success conditions, failure modes, and the tradeoff versus the sibling tool. Schema covers parameters; description covers behavior and output. Nothing essential for a grounded answer tool 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?

The input schema covers 100% of parameters, including all aliases for question, so the schema already carries the semantic load. The description does not add parameter-level detail beyond noting the routing and argument-filling behavior, which is sufficient but not augmentative. This matches the baseline for complete 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 a specific behavior: a grounded, hallucination-resistant answer mode that extracts answers only from tool results, and explicitly contrasts itself with ask_pipeworx. It names the resource (the pipeworx tool ecosystem) and the special guarantee, making it easy to distinguish from siblings.

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 gives explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on' and lists example domains. It also provides a clear when-not-to-use: 'prefer ask_pipeworx for casual lookups' due to the extra LLM call cost. This fully equips an agent to choose appropriately.

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

A3.8/5.0
Disambiguation3/5

Several tools occupy overlapping semantic space: ask_pipeworx, ask_pipeworx_beta (currently identical), and ask_pipeworx_grounded are the same router in different modes, while suggest_questions and discover_tools both serve capability discovery. The detailed descriptions help, but an agent could easily select the wrong entry point, especially among the prediction-market and router variants.

Naming Consistency4/5

All tool names use lowercase snake_case and mostly follow a verb_noun pattern (fetch_schema, resolve_entity, validate_claim), with some domain-prefixed nouns (polymarket_edges, pipeworx_trending) and a few adjective_noun outliers (recent_alerts, recent_changes). The convention is consistent and predictable, with only minor deviations.

Tool Count2/5

At 35 tools, the set is far too large for a server named Schemastore — only four tools relate to the schema catalog while the rest form a sprawling data-research, prediction-market, memory, and subscription platform. The count is heavy and the scope feels unfocused.

Completeness3/5

The broad data-research and prediction-market domain is well covered — lookup, compare, validate, research, arbitrage, subscriptions, and memory all have lifecycle support — but the server's namesake purpose (schema catalog) is thinly served by four read-only tools with no way to contribute or manage schemas. The domain mismatch makes the surface feel both over- and under-complete.

Resources