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Glama

Blockstream Info

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

A4.4/5.0
Behavior5/5

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds crucial behavioral context: it returns a structured verdict (confirmed, refuted, etc.), actual value with citation, and reasoning; it explains the distinction between could_not_verify (a failed check, not evidence) and unsupported (no source exists); and it discloses potential LLM/source failure with verification_error. This goes well beyond the annotations and helps the caller avoid misinterpreting results.

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 long but front-loaded with query examples and purpose, then proceeds through routing, return values, and critical caveats. Every sentence is informative, though some parts (e.g., 'Replaces 4–6 sequential calls') are optional context. It is well-structured and not redundant, but could be tightened.

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?

There is no output schema, so the description carries the full burden of explaining what the tool returns—it enumerates the verdict values, the actual value with citation, and reasoning. It also covers error semantics and the two routing paths, giving the caller a complete mental model. For a tool of this complexity (two params, one required), this is very complete.

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 schema covers 100% of parameters with detailed descriptions: claim has examples, tolerance_pct has range and override behavior. The tool description itself does not add extra parameter-level meaning beyond what the schema provides, so the baseline 3 applies. No gaps to compensate for.

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 examples ('Is it true that…', 'fact check') and states the tool performs 'claim verification against authoritative sources.' It clearly distinguishes itself from siblings by describing the SEC EDGAR fast path for company-financial claims and the grounded fallback for all other facts, and notes it replaces 4–6 sequential calls. This is a specific verb+resource with clear scope and differentiation.

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?

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct,' providing a clear when-to-use. It also explains the internal routing logic (financial vs. other claims). However, it does not explicitly name an alternative tool for non-claim queries or state a when-not, so it stops short of the full explicit alternatives guidance.

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

B3.1/5.0
Disambiguation1/5

The tool set includes multiple near-identical tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and several overlapping entity/profile tools (entity_profile, compare_entities, resolve_entity, validate_claim) that make it hard for an agent to choose the right one. The Bitcoin-specific tools are isolated and don't integrate well with the rest, creating two separate domains.

Naming Consistency2/5

Naming conventions are mixed: some tools use snake_case (ask_pipeworx, fee_estimates), some use underscores (polymarket_edges), and others are standalone verbs (remember, recall, forget). No consistent pattern is followed across the tool set.

Tool Count2/5

With 39 tools, the server is overstuffed for its stated purpose ('Blockstream Info'). The Bitcoin blockchain tools are only 7, while the rest are a sprawling external data platform (Pipeworx) with many redundant tools, making the number feel excessive and unfocused.

Completeness2/5

For a Bitcoin-oriented server, the blockchain tools cover basic operations (address, block, transaction, fee_estimates) but miss essentials like UTXO queries or block details. Meanwhile, the Pipeworx tools dominate and are overcomplete for a server that should be lightweight. The mismatch leaves the Bitcoin part incomplete.

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