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Glama

Openf1

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

A4.8/5.0
Behavior5/5

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

Even though annotations (readOnlyHint, openWorldHint, idempotentHint) already cover the read-only and non-destructive nature, the description adds substantial value: it details the two processing paths (structured SEC/XBRL vs grounded pipeline), enumerates possible verdicts, and explicitly warns that could_not_verify means the check did not happen and must not be treated as evidence. This is exactly the kind of behavioral nuance agents need.

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 long but every sentence earns its place. It is front-loaded with trigger phrases, then explains the fallback, verdict list, and critical caveats. The structure is logical and scannable, making it easy for an agent to parse. No redundant filler.

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?

The tool is highly complex (two paths, multiple verdicts, error semantics), yet the description fully covers how it works, what it returns (verdict, value, citation, reasoning), and edge cases like could_not_verify and unsupported. With no output schema, the description carries the full burden of explaining return behavior, and it does so thoroughly.

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?

Schema coverage is 100% and both parameters are described in the schema, but the description enriches them: it explains tolerance_pct overrides the implied tolerance, suggests 1–2 for hallucination detection, and clarifies the default behavior (capped at 5). This goes beyond param name/type and provides operational context.

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 uses specific verbs like 'validate', 'fact check', 'verify' and clearly identifies the resource: natural-language claims checked against authoritative sources. It distinguishes the tool from siblings by focusing on returning verdicts (confirmed, refuted, etc.) and mentions domain-specific paths (SEC EDGAR for financial claims, grounded pipeline for others). This goes well beyond a mere restatement of the name.

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 explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct,' providing clear context for when to invoke. It also notes it replaces 4–6 sequential calls, implying efficiency benefits. However, it does not explicitly name alternative sibling tools or state exclusions, so it's slightly below a perfect score for not offering direct comparisons.

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

A3.5/5.0
Disambiguation2/5

Several tool clusters have unclear boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions across the same 5,756-tool catalog, and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. The polymarket_* family also overlaps heavily (edges, arbitrage, fill_risk, kalshi_spread all surface tradeable discrepancies), and entity_profile/compare_entities/recent_changes all fan out to overlapping SEC/news/patent sources. Descriptions are detailed, but the set contains intentional near-duplicates that make selection genuinely ambiguous.

Naming Consistency2/5

Naming follows no single convention: get_drivers/get_laps/get_meetings/get_sessions use verb_noun, polymarket_arbitrage/polymarket_edges are noun-phrase domain prefixes, pipeworx_feedback/pipeworx_trending use a vendor prefix, remember/recall/forget are bare verbs, and ask_pipeworx variants mix with action phrases like discover_tools, deep_research, and validate_claim. The inconsistency makes it harder to predict related tool names.

Tool Count2/5

35 tools is heavy, and the count is severely mismatched to the server name 'Openf1': only 4 of 35 tools (get_drivers, get_laps, get_meetings, get_sessions) are actually F1-related, while the other 31 are a general-purpose Pipeworx data/prediction-market platform. The number itself could be reasonable for a broad data hub, but for an F1 server it is bloated with unrelated functionality.

Completeness2/5

For the stated F1 domain, the surface is severely incomplete: it covers meetings, sessions, drivers, and laps but lacks race results, qualifying results, standings, pit stops, or constructor data — major gaps for any F1 use case. The Pipeworx side is far more complete (query, research, entities, verification, memory, subscriptions), but that completeness doesn't serve the server's apparent purpose.

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