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Glama

Bitstamp

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 the readOnly/idempotent annotations, the description discloses key behavioral semantics: the distinction between could_not_verify (check did not happen) and unsupported (no source covers it), the routing behavior, and the tolerance override. It also explains that could_not_verify must not be shown as evidence, which is critical for safe usage.

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 well-structured and front-loaded with the purpose and common phrasings. It is somewhat lengthy, but every sentence (including the IMPORTANT note and the 'Replaces 4–6 calls' value prop) earns its place for a complex tool.

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 fully explains what is returned: the list of possible verdicts, the actual value with pipeworx:// citation, and reasoning. It also covers edge-case semantics (could_not_verify vs unsupported), routing, and tolerance behavior, making it complete 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?

The input schema already provides thorough descriptions for both parameters (100% coverage). The description adds extra value for tolerance_pct by explaining how to set it for hallucination detection and the default cap, and gives concrete examples for claim.

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 clearly states this is a natural-language claim verification tool with specific verb+resource (verify claims against authoritative sources). It provides example phrasings and explicitly differentiates from general-purpose tools by noting it replaces 4–6 sequential calls for fact-checking, making it the dedicated fact-check tool.

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 instructs to use 'whenever the agent needs to check whether something a user said is factually correct' and details routing between the SEC EDGAR structured path and the grounded pipeline. It gives good contextual guidance, though it does not explicitly name alternative sibling tools to avoid.

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

A3.7/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions, while the polymarket_edges, polymarket_arbitrage, and related tools blur edge-detection boundaries. The server's Bitstamp identity also clashes with the bulk of tools being unrelated data-research, making selection harder.

Naming Consistency3/5

Tool names are all lowercase snake_case, which is consistent formatting, but no coherent verb_noun pattern emerges. Some are verb-first (ask_pipeworx, compare_entities, discover_tools) while others are noun-first or resource-based (ticker_hour, order_book, polymarket_edges), and the naming style differs across the two major domains.

Tool Count2/5

At 38 tools, the set is well over the 15-tool threshold for a focused server, and the majority of tools are unrelated to the server's Bitstamp name. The count feels bloated and scattershot—it would be better split into separate data-research and exchange servers.

Completeness3/5

The data-research and question-answering surface is broadly covered, with meta-tools and validation. However, the Bitstamp exchange half is incomplete: it only provides public market data (ticker, order book, trades, OHLC) with no trading, account, or private-data operations, an obvious gap given the server's name.

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