Skip to main content
Glama

Crypto Fng

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?

Beyond read-only/idempotent annotations, the description discloses critical failure semantics: 'could_not_verify means the check did not happen... it is NOT evidence for or against the claim', and distinguishes 'unsupported' as a coverage gap. It also reveals the two data paths (SEC EDGAR/XBRL and grounded pipeline).

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 well-structured: it front-loads trigger phrases, then explains usage, return values, and caller warnings. While long, every section serves a purpose; the trigger phrase list is arguably redundant but aids detection.

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 no output schema, the description fully specifies the verdict enumeration, the special semantics of could_not_verify and unsupported, the inclusion of evidence citations and reasoning, and the automatic fallback behavior, making it complete for an agent to use.

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?

Input schema already covers both parameters at 100% with detailed descriptions. The description adds routing context for the claim parameter but does not add meaningful beyond-schema detail for parameter semantics, so baseline 3 is appropriate.

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 performs natural-language claim verification against authoritative sources, cites specific verdict types and a fast path vs grounded pipeline, and distinguishes itself from sibling research tools by focusing on fact-checking.

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 says 'Use whenever the agent needs to check whether something a user said is factually correct' and explains automatic routing between company-financial and other claims, plus notes it replaces 4–6 sequential calls, giving clear when-to-use and efficiency context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Crypto Fng toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.7/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all route questions through the same underlying catalog with only subtle differences. The polymarket family (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread, bet_research) also has fuzzy boundaries. Only the Crypto Fear & Greed tools are clearly distinct.

Naming Consistency3/5

Most tools follow a snake_case verb_noun pattern, but there are notable deviations: the ask_pipeworx_* family uses object-style names, pipeworx_feedback and polymarket_edges are noun_noun, and current_index vs index_history uses 'index' inconsistently. The polymarket_* family mixes verb and noun styles internally.

Tool Count2/5

33 tools is heavy for a server ostensibly named 'Crypto Fng' — only 2 of the tools relate to that core purpose. The rest constitute a broad generic data-research and prediction-market platform that would be more appropriately scoped as its own server. The count exceeds the comfortable range for an agent to reason over.

Completeness4/5

Within the actual (broad) domain, the surface is fairly complete: discovery (discover_tools, suggest_questions), querying (ask_pipeworx family), grounded verification (validate_claim, ask_pipeworx_grounded), deep research, entity resolution/profile/comparison, memory lifecycle, and subscription lifecycle. The named crypto-sentiment domain is fully covered with current and historical index tools, though the underlying 5,708 pack tools are only reachable indirectly through the router.

Resources