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Mempool Space

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.

TDQS

A4.5/5.0
Behavior5/5

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

The description goes well beyond annotations by explaining the verdict semantics, especially the critical caveat that 'could_not_verify' means the check did not happen and must not be treated as evidence. It also discloses the routing logic, the use of live sources, verbatim evidence, and the exact output components. This is valuable context not available in the readOnly/idempotent annotations, and there is no contradiction between the description and 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?

The description is information-dense yet well-organized: it front-loads trigger phrases and purpose, then explains the dual pipeline, output format, and a crucial caller warning. Every sentence contributes new information without redundancy. The length is justified by the tool's complexity.

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 takes on the burden of explaining return values and does so thoroughly: it lists all possible verdicts, mentions the citation and reasoning, and clarifies the difference between 'could_not_verify' and 'unsupported'. It also covers the internal process and error semantics, making the description complete for a complex tool.

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 fully documents both parameters with examples and detailed semantics (100% coverage). The description adds only a passing reference to 'exact percent-delta math' relative to tolerance but does not substantially enrich the schema-provided meaning. Therefore, the baseline for high schema coverage is the correct score.

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 identifies the tool as a natural-language claim verification service with explicit triggering phrases ('fact check', 'verify the claim'). It states the core verb+resource ('verify claim against authoritative sources') and distinguishes it from siblings by framing it as a fact-checker rather than a general Q&A or data-retrieval tool. The addition of 'Replaces 4–6 sequential calls' reinforces its unique role.

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 provides a clear when-to-use condition: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also distinguishes between financial claims (SEC EDGAR fast path) and other claims (grounded pipeline). However, it does not explicitly name alternatives or state when-not-to-use cases, which keeps it a step below a perfect score.

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
Disambiguation2/5

The set mixes two unrelated domains — Bitcoin mempool explorer tools and the much larger Pipeworx data-query platform — and within the Pipeworx half several tools route to the same 5,743-tool catalog (ask_pipeworx, deep_research, discover_tools, suggest_questions). ask_pipeworx_beta is currently an exact behavioral duplicate of ask_pipeworx, and the polymarket_* family has fuzzy boundaries (arbitrage vs edges vs fill_risk, with fill-checking living in both polymarket_arbitrage and polymarket_fill_risk), so an agent must read long descriptions to avoid misselection.

Naming Consistency3/5

All names use snake_case and there are recognizable sub-families (get_* Bitcoin lookups, ask_pipeworx_*, polymarket_*), but conventions are mixed across the whole set: bare-noun state tools (block_height, hashrate, mempool_stats, mining_pools) sit beside verb_noun actions (get_block, list_subscriptions), and prefix placement is inconsistent (ask_pipeworx vs pipeworx_trending/pipeworx_feedback). The naming is readable but not predictable enough to guess a tool's name from its function.

Tool Count2/5

41 tools is well past the 25+ threshold for a coherent server, and the count is inflated by bundling two unrelated products under a server named after only the smaller half (~10 Bitcoin tools vs ~31 Pipeworx tools). Many Pipeworx tools are convenience wrappers around one universal router, adding surface area without adding genuinely new capabilities.

Completeness3/5

Each half is internally workable: the Bitcoin side covers blocks, transactions, addresses, fees, hashrate, and pools, while the Pipeworx side provides broad query, research, subscription, and memory lifecycles. However, there are notable gaps relative to each domain (no block-list/fee-history endpoints on the explorer side; no direct per-source CRUD on the data side), and no single coherent domain is fully served because the server's stated identity matches only a fraction of its tools.

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