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Glama

Mychem

by io.github.pipeworx-io

Validate Claim

validate_claim
Read-onlyIdempotent

"Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. Company-financial claims (revenue, net income, cash for public US companies) verify via the structured SEC EDGAR + XBRL fast path with exact percent-delta math; ANY OTHER factual claim (macro statistics, rates, prices, drug data, records) automatically falls through to the grounded pipeline — routed to the right live source, answered with verbatim evidence, then judged. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported / could_not_verify), the grounded or structured actual value with pipeworx:// citation, and reasoning. IMPORTANT for callers: could_not_verify means the check did not happen (our LLM or source failed) and carries verification_error{stage,detail} — it is NOT evidence for or against the claim, and must not be shown as one. unsupported means we looked and cover no source for it. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
claimYesNatural-language factual claim, e.g., "Apple's FY2024 revenue was $400 billion" or "Microsoft made about $100B in profit last year".
tolerance_pctNoMax percent deviation still graded approximately_correct (0.5–50). Overrides the tolerance implied by the claim wording — set 1–2 for hallucination detection where any material error must be refuted. Default: implied by wording, capped at 5.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / properties / tolerance_pct
      Added value: +{
+  "description": "Max percent deviation still graded approximately_correct (0.5–50). Overrides the tolerance implied by the claim wording — set 1–2 for hallucination detection where any material error must be refuted. Default: implied by wording, capped at 5.",
+  "type": "number"
+}
  2. First observed

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 goes beyond by explaining important edge-case semantics: could_not_verify is NOT evidence and carries a verification_error; unsupported means no source is covered. It also reveals the routing logic between SEC EDGAR/XBRL and the grounded pipeline, which is not visible in 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 packed with useful information, with no obvious fluff. It is front-loaded with examples and purpose. However, it is a long single paragraph rather than structured bullets; breaking it into sections could improve scanability, but the content justifies its length.

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?

Even without an output schema, the description fully explains return values (verdict categories, actual value, citation, reasoning), error semantics for could_not_verify, and the difference between unsupported and refuted. It covers the tool's scope and edge cases comprehensively for a tool of this complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema descriptions already cover both parameters (claim and tolerance_pct) at 100%. The description adds value by clarifying how tolerance_pct overrides claim wording and its default behavior, plus providing example claims in the schema. It reinforces and extends the schema meaning without substantial gap.

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 opens with concrete natural-language examples and explicitly states the verb+resource: verifying factual claims against authoritative sources. It clearly differentiates from sibling tools (e.g., ask_pipeworx, deep_research) by focusing on claim verification and by mentioning the two distinct pipelines for financial vs. other claims.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives an explicit usage directive: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also contrasts the financial fast path with the grounded pipeline, and notes that this tool replaces 4–6 sequential calls. However, it does not explicitly name alternatives or state when NOT to use it, which would push it to a 5.

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

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