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Glama

Coingecko

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

A4.5/5.0
Behavior5/5

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

The description goes beyond annotations by defining the error semantics: "could_not_verify means the check did not happen... it is NOT evidence for or against the claim, and must not be shown as one." It also distinguishes "unsupported" and explains the fast-path vs grounded pipeline behavior. This adds substantial context beyond the readOnly/openWorld/idempotent annotations.

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?

Though ~250 words, every sentence adds value: trigger phrases, routing logic, return payload, error distinction, and efficiency claim. It's front-loaded with the core purpose and structured with clear sections, making it comprehensive without waste.

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?

Without an output schema, the description fully specifies the verdict enum, the inclusion of a pipeworx:// citation, reasoning, and the verification_error payload for could_not_verify. It also covers both claim categories and the fallthrough behavior, making it self-sufficient for an agent to invoke correctly.

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 already covers both parameters with rich descriptions (claim example, tolerance_pct range and override behavior), so the description needn't repeat them. The description does contextualize tolerance as "exact percent-delta math" for financial claims, but the schema already provides semantics. Baseline 3 applies for full 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 opens with concrete natural-language triggers ("Is it true that…" / "fact check" / "verify the claim that…") and states it performs "natural-language claim verification against authoritative sources," immediately distinguishing it from generic querying tools. It also explains the two routing paths (SEC EDGAR fast path vs grounded pipeline) and the verdict output, leaving no doubt about the tool's function.

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 says "Use whenever the agent needs to check whether something a user said is factually correct," giving a clear trigger condition. The description also clarifies that it replaces 4–6 sequential calls, implying it's the consolidated tool for claim verification, but it doesn't enumerate exclusions or explicitly compare against siblings like ask_pipeworx_grounded or deep_research.

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

A3.6/5.0
Disambiguation2/5

Several tools are near-duplicates or have heavily overlapping responsibilities (ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded, polymarket_arbitrage / polymarket_edges / polymarket_fill_risk, and ai_visibility_check / scan_competitor_ai_presence). The many meta/entry-point tools (discover_tools, suggest_questions, pipeworx_trending) also blur the boundary between discovery and execution.

Naming Consistency2/5

Tool names mix imperative verb-first patterns (get_coin, search_coins, validate_claim) with noun-phrase labels (bet_research, entity_profile, pipeworx_trending, polymarket_edge_tracker) and inconsistent prefixes. Snake_case is consistent, but the naming grammar and verb styles are not.

Tool Count2/5

35 tools is well past the comfortable range, and the count feels inflated by duplicate routing modes, overlapping polymarket scanners, and generic memory/meta utilities. A server nominally named Coingecko carries only 4 crypto tools while the overwhelming majority belong to unrelated domains.

Completeness2/5

As a CoinGecko server, the surface is severely incomplete: search/get/market/trending exist but historical prices, OHLC, exchanges, categories, and coin details are missing. As a broader data/prediction-market utility it is more expansive, but the lack of a coherent domain makes coverage impossible to assess as a single product.

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