Skip to main content
Glama

Hunter

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 annotations (readOnly, openWorld, idempotent), the description discloses the meaning of each verdict, especially the critical distinction between 'could_not_verify' (check did not happen) and 'unsupported' (no source found). It also warns that 'could_not_verify' must not be shown as evidence, which is essential behavioral context for an AI agent.

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 information-dense; the opening examples front-load the purpose, and every subsequent sentence contributes behavioral or usage detail. The 'Replaces 4–6 sequential calls' sentence and the verdict-explanations are valuable, though a slightly shorter version could achieve the same clarity.

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?

Given the tool's complexity and lack of an output schema, the description sufficiently covers return values (verdict list, actual value with citation, reasoning), special edge cases (could_not_verify vs. unsupported), and parameter behavior. It leaves no major gaps for an agent to invoke and interpret results correctly.

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?

Schema coverage is 100%, so the baseline is 3. The description adds extra meaning for tolerance_pct by explaining how it overrides the claim wording's implied tolerance and recommending 1–2 for hallucination detection, which goes beyond the schema's generic range description. It also clarifies the default cap of 5.

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 natural-language phrasings and a clear verb ('validate a claim'), defines the resource as a factual claim, and distinguishes from sibling tools by stating it replaces 4–6 sequential calls. It explicitly differentiates the company-financial fast path from the general grounded pipeline, making the tool's scope unambiguous.

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 clearly states when to use the tool: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains internal routing (SEC EDGAR vs. grounded pipeline) and notes it replaces sequential calls, but does not explicitly name sibling tools as alternatives or exclusions, so it stops short of a full when-not/alternatives breakdown.

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

All Hunter 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

A3.6/5.0
Disambiguation2/5

Several tool clusters have genuinely blurry boundaries: ask_pipeworx, ask_pipeworx_beta (explicitly 'currently matches ask_pipeworx exactly'), ask_pipeworx_grounded, and deep_research all route questions to the same 5,743-tool catalog, and the six Polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) heavily overlap on 'should I bet / where is the edge'. The descriptions are detailed and cross-reference each other, but an agent would still struggle to pick correctly among near-duplicates.

Naming Consistency3/5

Everything is uniformly snake_case and readable, but there is no consistent verb_noun pattern: verb_noun (resolve_entity, discover_tools, validate_claim) mixes with noun_noun (domain_search, entity_profile, polymarket_arbitrage), adjective_noun (deep_research, recent_changes), bare verbs (forget, recall, remember), and brand prefixes (pipeworx_*, ask_pipeworx_*). Readable, but patternless.

Tool Count2/5

34 tools is well above the heavy threshold, but the bigger issue is scope incoherence: a server named 'Hunter' dedicates only 3 of 34 tools to Hunter.io email lookup while the remaining 31 belong to an unrelated Pipeworx data-research/prediction-market platform. The count is not earned by a single coherent purpose.

Completeness3/5

The Pipeworx research core is quite thorough: entity resolution, single-lookup, grounded answers, deep research, profiles, comparisons, claim validation, semantic search-within-records, change feeds, subscriptions, and memory form a full research lifecycle. However, the domain is a grab-bag spanning email finding, npm dependency checking, prediction markets, and data research, and the three Hunter.io tools that match the server name are only a thin fragment of the surface.

Resources