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Glama

Schemastore

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

A5/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses critical behavioral traits: the two routing paths, the exact verdict enum, and the crucial distinction between could_not_verify (check did not happen) and unsupported (no source exists). The explicit warning that could_not_verify must not be shown as evidence is high-value transparency.

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 longer than average but every sentence adds necessary detail: trigger phrases, routing rules, output format, error semantics, and efficiency rationale. It is front-loaded with examples and logically organized, making it easy to scan and apply without bloat.

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?

With no output schema, the description must explain return values and does so thoroughly: verdict list, grounded/structured value with citation, reasoning, and error object for verification failures. It also covers edge cases (could_not_verify vs unsupported) and routes for both company-financial and other claims, making it complete for the tool's complexity.

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

Parameters5/5

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

Even though schema coverage is 100%, the description enriches both parameters: claim is already exemplified, and tolerance_pct gains real-world meaning (override implied tolerance, set 1–2 for hallucination detection, default cap of 5). This guidance is actionable and goes beyond the schema's basic type/description.

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 defines the tool as natural-language claim verification with explicit trigger phrases ('fact check', 'verify the claim that…') and a specific outcome (verdict + evidence). It clearly distinguishes this from sibling research/entity tools by focusing on judging the truth of a stated claim rather than open-ended or comparative analysis.

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?

States 'Use whenever the agent needs to check whether something a user said is factually correct' and further splits guidance: company-financial claims take the structured SEC/XBRL path, all other claims use the grounded pipeline. It also positions the tool as a replacement for 4–6 sequential calls, indicating when it is the preferred single-call option.

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.

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