Skip to main content
Glama

Solscan

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

A4.4/5.0
Behavior5/5

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

The description goes far beyond annotations by disclosing the two-path behavior (structured vs. grounded), the full verdict vocabulary, and a critical caller warning that could_not_verify means the check did not happen and must not be treated as evidence. It also clarifies the meaning of unsupported, which provides essential behavioral transparency beyond the readOnly/openWorld annotations.

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, starting with user intent phrasings, then usage, then behavioral details and a crucial warning. It is somewhat lengthy but each section earns its place; the caller warning is essential and the final line about replacing sequential calls provides value.

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?

Despite having no output schema, the description fully covers the return values (verdict, actual value, citation, reasoning) and explains edge cases such as could_not_verify and unsupported. The tool's complexity (two routing paths, multiple verdict types) is well-documented, making the description sufficient for an agent to know when and how to invoke it.

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 describes both parameters with 100% coverage, so the baseline is 3. The description adds no parameter-specific semantic detail; it references percent-delta math but does not explain tolerance_pct beyond what the schema already provides.

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 defines the tool as natural-language claim verification against authoritative sources, with a specific verb ('validate'/'fact check') and resources (SEC EDGAR for financial claims, grounded pipeline for others). It also provides example phrasings and differentiates the tool's scope from general research by focusing on factual validation and verdicts.

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 states when to use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also distinguishes two routing paths (financial vs. other claims), but it does not explicitly name alternatives or exclusion cases, so it stops short of a 5.

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

All Solscan 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

A3.5/5.0
Disambiguation2/5

Several tools are near-duplicates: ask_pipeworx_beta is explicitly described as currently identical to ask_pipeworx, and multiple prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage) overlap in purpose. The Solana-specific tools are distinct, but the large non-Solana cluster creates real ambiguity.

Naming Consistency3/5

All tools use snake_case, but naming styles vary widely: get_/list_ verbs, ask_pipeworx family, polymarket_* cluster, and descriptive noun-style names like entity_profile, validate_claim, generate_llms_txt, scan_dependency. There is no single consistent verb_noun convention across the set.

Tool Count1/5

With 36 tools, the count is high, but the critical problem is scope mismatch: a server named Solscan has only 5 Solana-related tools, while 31 are unrelated data/research/prediction-market utilities. This makes the tool count inappropriate for the apparent purpose.

Completeness2/5

As a Solana explorer, the set covers only account details, token holdings, token metadata, transactions, and transfers; major gaps include blocks, token price/history, NFTs, programs/staking, and more comprehensive transfer history. For the broader data-research theme, coverage is broad but scattered and lacks a single coherent domain.

Resources